ALBERT

Description: Provides a listing of current staff, committee members and society officers.

Description: We develop an approach to quantitative analysis of carbon dioxide concentration in exhaled breath, recorded as a function of time by capnography. The generated waveform—or capnogram—is currently used in clinical practice to establish the presence of respiration as well as determine respiratory rate and end-tidal CO 2 concentration. The capnogram shape also has diagnostic value, but is presently assessed qualitatively, by visual inspection. Prior approaches to quantitatively characterizing the capnogram shape have explored the correlation of various geometric parameters with pulmonary function tests. These studies attempted to characterize the capnogram in normal subjects and patients with cardiopulmonary disease, but no consistent progress was made, and no translation into clinical practice was achieved. We apply automated quantitative analysis to discriminate between chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF), and between COPD and normal. Capnograms were collected from 30 normal subjects, 56 COPD patients, and 53 CHF patients. We computationally extract four physiologically based capnogram features. Classification on a hold-out test set was performed by an ensemble of classifiers employing quadratic discriminant analysis, designed through cross validation on a labeled training set. Using 80 exhalations of each capnogram record in the test set, performance analysis with bootstrapping yields areas under the receiver operating characteristic (ROC) curve of 0.89 (95% CI: 0.72–0.96) for COPD/CHF classification, and 0.98 (95% CI: 0.82–1.0) for COPD/normal classification. This classification performance is obtained with a run time sufficiently fast for real-time monitoring.

Description: In this study, we used muscle and motor unit indices, derived from convenient surface electromyography (EMG) measurements, for examination of paretic muscle changes post stroke. For 12 stroke subjects, compound muscle action potential and voluntary surface EMG signals were recorded from paretic and contralateral first dorsal interosseous, abductor pollicis brevis, and abductor digiti minimi muscles. Muscle activation index (AI), motor unit number index (MUNIX), and motor unit size index (MUSIX) were then calculated for each muscle. There was a significant AI reduction for all the three muscles in paretic side compared with contralateral side, providing an evidence of muscle activation deficiency after stroke. The hand MUNIX (defined by summing the values from the three muscles) was significantly reduced in paretic side compared with contralateral side, whereas the hand MUSIX was not significantly different. Furthermore, diverse changes in MUNIX and MUSIX were observed from the three muscles. A major feature of the present examinations is the primary reliance on surface EMG, which offers practical benefits because it is noninvasive, induces minimal discomfort and can be performed quickly.

Description: Robotic needle steering systems have the potential to greatly improve medical interventions, but they require new methods for medical image guidance. Three-dimensional (3-D) ultrasound is a widely available, low-cost imaging modality that may be used to provide real-time feedback to needle steering robots. Unfortunately, the poor visibility of steerable needles in standard grayscale ultrasound makes automatic segmentation of the needles impractical. A new imaging approach is proposed, in which high-frequency vibration of a steerable needle makes it visible in ultrasound Doppler images. Experiments demonstrate that segmentation from this Doppler data is accurate to within 1–2 mm. An image-guided control algorithm that incorporates the segmentation data as feedback is also described. In experimental tests in ex vivo bovine liver tissue, a robotic needle steering system implementing this control scheme was able to consistently steer a needle tip to a simulated target with an average error of 1.57 mm. Implementation of 3-D ultrasound-guided needle steering in biological tissue represents a significant step toward the clinical application of robotic needle steering.

Description: This paper presents a noncontact capacitive sensing system (C-Sens) for locomotion mode recognition of transtibial amputees. C-Sens detects changes in physical distance between the residual limb and the prosthesis. The sensing front ends are built into the prosthetic socket without contacting the skin. This novel signal source improves the usability of locomotion mode recognition systems based on electromyography (EMG) signals and systems based on capacitance signals obtained from skin contact. To evaluate the performance of C-Sens, we carried out experiments among six transtibial amputees with varying levels of amputation when they engaged in six common locomotive activities. The capacitance signals were consistent and stereotypical for different locomotion modes. Importantly, we were able to obtain sufficiently informative signals even for amputees with severe muscle atrophy (i.e., amputees lacking of quality EMG from shank muscles for mode classification). With phase-dependent quadratic classifier and selected feature set, the proposed system was capable of making continuous judgments about locomotion modes with an average accuracy of $96.3%$ and $94.8%$ for swing phase and stance phase, respectively (Experiment 1). Furthermore, the system was able to achieve satisfactory recognition performance after the subjects redonned the socket (Experiment 2). We also validated that C-Sens was robust to load bearing changes when amputees carried 5-kg weights during activities (Experiment 3). These results suggest that noncontact capacitive sensing is capable of circumventing practical problems of EMG systems without sacrificing performance and it is, thus, promising for automatic recognition of human motion intent for controlling powered prostheses.

Description: This study aims to analyze the protein aggregates spatial distribution for different cataract degrees, and correlate this information with the lens acoustical parameters and by this way, assess the cataract regional hardness. Different cataract degrees were induced ex vivo in porcine lenses. A 25 MHz ultrasonic transducer was used to obtain the acoustical parameters (velocity, attenuation, and backscattering signals). B-scan and Nakagami images were constructed. Also, lenses with different cataract degrees were sliced in two regions (nucleus and cortex), for fibers and collagen detection. A significant increase with cataract formation was found for the velocity, attenuation, and brightness intensity of the B-scan images and Nakagami m parameter ( $p 〈 0.01$ ). The acoustical parameters showed a good to moderate correlation with the m parameter for the different stages of cataract formation. A strong correlation was found between the protein aggregates in the cortex and the m parameter. Lenses without cataract are characterized using a classification and regression tree, by a mean brightness intensity ≤0.351, a variance of the B-scan brightness intensity ≤0.070, a velocity ≤1625 m/s, and an attenuation ≤0.415 dB/mm·MHz (sensitivity: 100% and specificity: 72.6%). To characterize different cataract degrees, the m parameter should be considered. Initial stages of cataract are characterized by a mean brightness intensity 〉0.351 and a variance of the m parameter 〉0.110. Advanced stages of cataract are characterized by a mean brightness intensity 〉0.351, a variance of the m parameter ≤0.110, and a mean m parameter 〉0.374. For initial and advanced stages of cataract, a sensitivity of 78.4% - nd a specificity of 86.5% are obtained.

Description: The substrate of potentially lethal cardiac arrhythmias often resides in the gray zone (GZ), a mixture of viable myocytes and collagen strands found between healthy myocardium and infarct core (IC). The specific aims of this paper are to demonstrate correspondence between regions delineated in T1 * (apparent T 1 ) maps and tissue characteristics seen in histopathology and to determine the MR imaging resolution needed to adequately identify GZ-associated substrate in chronic infarct. For this, a novel 3-D multicontrast late enhancement (MCLE) MR method was used to image ex vivo swine hearts with chronic infarction, at high resolution ( $0.6times 0.6times 1.25$ mm). Pixel-wise classified tissue maps were calculated using steady-state and T ${{bf _1}^{*}}$ images as input to a fuzzy-clustering algorithm. Quantitative histology based on collagen stains was performed in $n = 10$ selected slabs and showed very good correlations between histologically-determined areas of heterogeneous and dense fibrosis, and the corresponding GZ ( $R^{2} = 0.96$ ) and IC ( $R^{2} = 0.97$ ) in tissue classified maps. Furthermore, in $n = 24$ slabs, we performed volumetric measurements of GZ and IC, at the original and decreased image resolutions. Our results demonstrated that the IC volume remained relatively unchanged across all resolutions, whereas the GZ volume progressively increased with diminished image resolution, with changes reaching significance at $1times 1times 5$ mm resolution ( $p 〈0.05$ ) but not at $1times 1times 2.5$ mm, suggesting that this resolution may be sufficient to adequately identify the GZ from MCLE images, enabling an effective MR probing of remodeled myocardium in late infarct. Future work will focus on translating these findings to optimizing the current in vivo MCLE imaging of the GZ.

Description: A control scheme was designed in order to reduce the risks of hyperglycemia and hypoglycemia in type 1 diabetes mellitus (T1DM). This structure is composed of three main components: an $mathcal {H}_{infty }$ robust controller, an insulin feedback loop (IFL), and a safety mechanism (SM). A control-relevant model that is employed to design the robust controller is identified. The identification procedure is based on the distribution version of the UVA/Padova metabolic simulator using the simulation adult cohort. The SM prevents dangerous scenarios by acting upon a prediction of future glucose levels, and the IFL modifies the loop gain in order to reduce postprandial hypoglycemia risks. The procedure is tested on the complete in silico adult cohort of the UVA/Padova metabolic simulator, which has been accepted by the Food and Drug Administration (FDA) in lieu of animal trials.

Description: Provides a listing of current committee members and society officers.

Description: Brain research requires a standardized brain atlas to describe both the variance and invariance in brain anatomy and neuron connectivity. In this study, we propose a system to construct a standardized 3D Drosophila brain atlas by integrating labeled images from different preparations. The 3D fly brain atlas consists of standardized anatomical global and local reference models, e.g., the inner and external brain surfaces and the mushroom body. The averaged global and local reference models are generated by the model averaging procedure, and then the standard Drosophila brain atlas can be compiled by transferring the averaged neuropil models into the averaged brain surface models. The main contribution and novelty of our study is to determine the average 3D brain shape based on the isosurface suggested by the zero-crossings of a 3D accumulative signed distance map. Consequently, in contrast with previous approaches that also aim to construct a stereotypical brain model based on the probability map and a user-specified probability threshold, our method is more robust and thus capable to yield more objective and accurate results. Moreover, the obtained 3D average shape is useful for defining brain coordinate systems and will be able to provide boundary conditions for volume registration methods in the future. This method is distinguishable from those focusing on 2D + Z image volumes because its pipeline is designed to process 3D mesh surface models of Drosophila brains.

Description: Electroanatomical mapping (EAM) systems are commonly used in clinical practice for guiding catheter ablation treatments of common arrhythmias. In focal tachycardias, the ablation target is defined by locating the earliest activation area determined by the joint analysis of electrogram (EGM) signals at different sites. However, this is currently a manual time-consuming and experience-dependent task performed during the intervention and thus prone to stress-related errors. In this paper, we present an automatic delineation strategy that combines electrocardiogram (ECG) information with the wavelet decomposition of the EGM signal envelope to identify the onset of each EGM signal for activation mapping. Fourteen electroanatomical maps corresponding to ten patients suffering from non-tolerated premature ventricular contraction (PVC) beats and admitted for ablation procedure were used for evaluation. We compared the results obtained automatically with two types of manual annotations: one during the intervention by an expert technician (on-procedure) and other after the intervention (off-procedure), free from time and procedural constraints, by two other technicians. The automatic annotations show a significant correlation (0.95, p $〈$ 0.01) with the evaluation reference (off-procedure annotation sets combination) and has an error of 2.1 $pm$ 10.9 ms, around the order of magnitude of the on-procedure annotations error ( $-$ 2.6 $pm$ 6.8 ms). The results suggest that the proposed methodology could be incorporated into EAM systems to considerably reduce processing time during ablation interventions.

Description: Sleepiness and fatigue can reach particularly high levels during long-haul overnight flights. Under these conditions, voluntary or even involuntary sleep periods may occur, increasing the risk of accidents. The aim of this study was to assess the performance of an in-flight automatic detection system of low-vigilance states using a single electroencephalogram channel. Fourteen healthy pilots voluntarily wore a miniaturized brain electrical activity recording device during long-haul flights ( $10 pm 2.0$ h, Atlantic 2 and Falcon 50 M, French naval aviation). No subject was disturbed by the equipment. Seven pilots experienced at least a period of voluntary ( $26.8 pm 8.0$ min, $n = 4$ ) or involuntary sleep (N1 sleep stage, $26.6 pm 18.7$ s, $n = 7$ ) during the flight. Automatic classification (wake/sleep) by the algorithm was made for 10-s epochs (O1-M2 or C3-M2 channel), based on comparison of means to detect changes in α, β, and θ relative power, or ratio [( $alpha +theta$ )/β], or fuzzy logic fusion (α, β). Pertinence and prognostic of the algorithm were determined using epoch-by-epoch comparison with visual-scoring (two blinded readers, AASM rules). The best concordance between automatic detection and visual-scoring was observed within the O1-M2 channel, using the ratio [( $alpha +theta$ )/β] ( $98.3 pm 4.1%$ of good detection, $K = 0.94 pm 0.07$ , with a $0.04 pm 0.04$ false positive rate and a $0.87 pm 0.10$ true positive rate). Our results confirm the efficiency of a miniaturized single electroencephalographic channel recording device, associated with an automatic detection algorithm, in order to detect low-vigilance states during real flights.

Description: Spectral CT has proven an important development in biomedical imaging, and there have been several publications in the past years demonstrating its merits in pre-clinical and clinical applications. In 2012, Xu reported that near-term implementation of spectral micro-CT could be enhanced by a hybrid architecture: a narrow-beam spectral “interior” imaging chain integrated with a traditional wide-beam “global” imaging chain. This hybrid integration coupled with compressive sensing (CS)-based interior tomography demonstrated promising results for improved contrast resolution, and decreased system cost and radiation dose. The motivation for the current study is implementation and evaluation of the hybrid architecture with a first-of-its-kind hybrid spectral micro-CT system. Preliminary results confirm improvements in both contrast and spatial resolution. This technology is shown to merit further investigation and potential application in future spectral CT scanner design.

Description: Tumors are typically analyzed as a single unit, despite their biologically heterogeneous nature. This limits correlations that can be drawn between regional variation and treatment outcome. Furthermore, despite the availability of high resolution 3-D medical imaging techniques, local outcomes, (e.g., tumor growth), are not easily measured. This paper proposes a method that uses streamlines to divide a 3-D region of interest (e.g., tumor) into units where local properties can be measured over the paths of growth. The parameters such as directional length and mean intensity can be measured locally at sequential time points and then compared. The method is evaluated on synthetic objects, simulated tumors, and medical images of brain tumors. The evaluations suggest that the method is suitable for mapping amorphous dynamic objects.

Description: Monitoring heart activity from electrocardiograms (ECG) is crucial to avoid unnecessary fatalities; therefore, detection of QRS complex is fundamental to automated ECG monitoring. Continuous, portable 24/7 ECG monitoring requires wireless technology with constraints on power, bandwidth, area, and resolution. In order to provide continuous remote monitoring of patients and fast transmission of data to medical personnel for instantaneous intervention, we propose a methodology that converts analog inputs into pulses for ultralow power implementation. The signal encoding scheme is the time-based integrate and fire (IF) sampler from which a set of signal descriptors in the pulse domain are proposed. Furthermore, a logical decision rule for QRS detection based on morphological checking is derived. The proposed decision logic depends exclusively on relational and logical operators resulting in ultrafast recognition and can be implemented using combinatorial logic hardware to guarantee power consumption orders of magnitude lower than any microprocessor device. The algorithm was evaluated using the MIT-BIH arrhythmia database and results show that our algorithm performance is comparable to the state-of-the art software-based detection.

Description: In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion–extension, abduction–adduction, and pronation–supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies $(R^{2})$ and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction–adduction DOF $(R^{2}_{rm constrained}$ = 90.8 ± 0.6, $R^{2}_{rm unconstrained}$ = 85.6 ± 1.6) and pronation–supination DOF ( $R^{2}_{rm constrained}$ = 88.5 ± 0.9, $R^{2}_{rm unconstrained}$ = 82.3 ± 1.6), but no significant difference was found in the flexion–extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time- and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable levels of muscle activation.

Description: This study proposes a novel hybrid brain–computer interface (BCI) approach for increasing the spelling speed. In this approach, the P300 and steady-state visually evoked potential (SSVEP) detection mechanisms are devised and integrated so that the two brain signals can be used for spelling simultaneously. Specifically, the target item is identified by 2-D coordinates that are realized by the two brain patterns. The subarea/location and row/column speedy spelling paradigms were designed based on this approach. The results obtained for 14 healthy subjects demonstrate that the average online practical information transfer rate, including the time of break between selections and error correcting, achieved using our approach was 53.06 bits/min. The pilot studies suggest that our BCI approach could achieve higher spelling speed compared with the conventional P300 and SSVEP spellers.

Description: Wearable and implantable wireless communication devices have in recent years gained increasing attention for medical diagnostics and therapeutics. In particular, wireless capsule endoscopy has become a popular method to visualize and diagnose the human gastrointestinal tract. Estimating the exact position of the capsule when each image is taken is a very critical issue in capsule endoscopy. Several approaches have been developed by researchers to estimate the capsule location. However, some unique challenges exist for in-body localization, such as the severe multipath issue caused by the boundaries of different organs, inconsistency of signal propagation velocity and path loss parameters inside the human body, and the regulatory restrictions on using high-bandwidth or high-power signals. In this paper, we propose a novel localization method based on spatial sparsity. We directly estimate the location of the capsule without going through the usual intermediate stage of first estimating time-of-arrival or received-signal strength, and then a second stage of estimating the location. We demonstrate the accuracy of the proposed method through extensive Monte Carlo simulations for radio frequency emission signals within the required power and bandwidth range. The results show that the proposed method is effective and accurate, even in massive multipath conditions.

Description: In the electroencephalogram (EEG) or magnetoencephalogram (MEG) context, brain source localization methods that rely on estimating second-order statistics often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. Due to the correlated background EEG/MEG signal, an adaptive approach to localization is desirable. Previous work has addressed these issues by reducing the adaptive degrees of freedom (DoFs). This reduction results in decreased resolution and accuracy of the estimated source configuration. This paper develops and tests a new multistage adaptive processing technique based on the minimum variance beamformer for brain source localization that has been previously used in the radar statistical signal processing context. This processing, referred to as the fast fully adaptive (FFA) approach, can significantly reduce the required sample support, while still preserving all available DoFs. To demonstrate the performance of the FFA approach in the limited data scenario, simulation and experimental results are compared with two previous beamforming approaches; i.e., the fully adaptive minimum variance beamforming method and the beamspace beamforming method. Both simulation and experimental results demonstrate that the FFA method can localize all types of brain activity more accurately than the other approaches with limited data.

Description: Accurate estimation of daily total energy expenditure (EE) is a prerequisite for assisted weight management and assessing certain health conditions. The use of wearable sensors for predicting free-living EE is challenged by consistent sensor placement, user compliance, and estimation methods used. This paper examines whether a single ear-worn accelerometer can be used for EE estimation under free-living conditions. An EE prediction model was first derived and validated in a controlled setting using healthy subjects involving different physical activities. Ten different activities were assessed showing a tenfold cross validation error of 0.24. Furthermore, the EE prediction model shows a mean absolute deviation below 1.2 metabolic equivalent of tasks. The same model was applied to a free-living setting with a different population for further validation. The results were compared against those derived from doubly labeled water. In free-living settings, the predicted daily EE has a correlation of $hbox{0.74}, p = hbox{0.008}$ , and a MAD of $hbox{27}, hbox{kcal/day}$ . These results demonstrate that laboratory-derived prediction models can be used to predict EE under free-living conditions.

Description: In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain–computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; $p = 0.007$ ) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; $p = 0.016$ ). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; $p = 0.013$ ), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation.

Description: Multi-element volume radio-frequency (RF) coils are an integral aspect of the growing field of high-field magnetic resonance imaging. In these systems, a popular volume coil of choice has become the transverse electromagnetic (TEM) transceiver coil consisting of microstrip resonators. In this paper, to further advance this design approach, a new microstrip resonator strategy in which the transmission line is segmented into alternating impedance sections, referred to as stepped impedance resonators (SIRs), is investigated. Single-element simulation results in free space and in a phantom at 7 T (298 MHz) demonstrate the rationale and feasibility of the SIR design strategy. Simulation and image results at 7 T in a phantom and human head illustrate the improvements in a transmit magnetic field, as well as RF efficiency (transmit magnetic field versus specific absorption rate) when two different SIR designs are incorporated in 8-element volume coil configurations and compared to a volume coil consisting of microstrip elements.

Description: Deep brain stimulation (DBS) is an established therapy for movement disorders, but the fundamental mechanisms by which DBS has its effects remain unknown. Computational models can provide insights into the mechanisms of DBS, but to be useful, the models must have sufficient detail to predict accurately the electric fields produced by DBS. We used a finite-element method model of the Medtronic 3387 electrode array, coupled to cable models of myelinated axons, to quantify how interpolation errors, electrode geometry, and the electrode–tissue interface affect calculation of electrical potentials and stimulation thresholds for populations of model nerve fibers. Convergence of the potentials was not a sufficient criterion for ensuring the same degree of accuracy in subsequent determination of stimulation thresholds, because the accuracy of the stimulation thresholds depended on the order of the elements. Simplifying the 3387 electrode array by ignoring the inactive contacts and extending the terminated end of the shaft had position-dependent effects on the potentials and excitation thresholds, and these simplifications may impact correlations between DBS parameters and clinical outcomes. When the current density in the bulk tissue is uniform, the effect of the electrode–tissue interface impedance could be approximated by filtering the potentials calculated with a static lumped electrical equivalent circuit. Further, for typical DBS parameters during voltage-regulated stimulation, it was valid to approximate the electrode as an ideal polarized electrode with a nonlinear capacitance. Validation of these computational considerations enables accurate modeling of the electric field produced by DBS.

Description: Electrograms (EGM) recorded from the surface of the myocardium are becoming more and more accessible. T-wave alternans (TWA) is associated with increased vulnerability to ventricular tachycardia/fibrillation and it occurs before the onset of ventricular arrhythmias. Thus, accurate methodologies for time-varying alternans estimation/detection in EGM are needed. In this paper, we perform a simulation study based on epicardial EGM recorded in vivo in humans to compare the accuracy of four methodologies: the spectral method (SM), modified moving average method, laplacian likelihood ratio method (LLR), and a novel method based on time-frequency distributions. A variety of effects are considered, which include the presence of wide band noise, respiration, and impulse artifacts. We found that 1) EGM-TWA can be detected accurately when the standard deviation of wide-band noise is equal or smaller than ten times the magnitude of EGM-TWA. 2) Respiration can be critical for EGM-TWA analysis, even at typical respiratory rates. 3) Impulse noise strongly reduces the accuracy of all methods, except LLR. 4) If depolarization time is used as a fiducial point, the localization of the T-wave is not critical for the accuracy of EGM-TWA detection. 5) According to this study, all methodologies provided accurate EGM-TWA detection/quantification in ideal conditions, while LLR was the most robust, providing better detection-rates in noisy conditions. Application on epicardial mapping of the in vivo human heart shows that EGM-TWA has heterogeneous spatio-temporal distribution.

Description: The experimental verification of a novel sensor topology capable of measuring both the position and energy of an electron beam inside a compact electron linear accelerator for radiotherapy is presented. The method applies microwave sensing techniques and allows for the noninterceptive monitoring of the respective beam parameters within compact accelerators for medical or industrial purposes. A state space feedback approach is described with the help of which beam displacements, once detected, can be corrected within a few system macropulses. The proof-of-principle experiments have been conducted with a prototype accelerator and customized hardware. Additionally, closed-loop operation with high accuracy is demonstrated.

Description: In human movement analysis based on stereophotogrammetry, bone pose is reconstructed by observing a cluster of skin markers. Each marker undergoes a displacement relative to the underlying bone that is regarded as an artefact (soft-tissue artefact, STA) since it affects accuracy in bone pose estimation. This paper proposes a set of metrics for the statistical description of the STA and its effects on cluster pose, size, and shape, with the intent of contributing to a clearer knowledge of its characteristics, and consequently of setting the bases for the development of more accurate bone pose estimators than presently available. Skin marker clusters behave as deformable bodies in motion relative to the underlying bone. Their motion can be described, based on Procrustes analysis, as the composition of four independent transformations: translation and rotation (rigid motion, RM), and change in size and shape (nonrigid motion, NRM). Statistical parameters describing the time histories of both the individual marker STA and the cluster transformations listed earlier were defined. For demonstration purposes, data collected ex vivo were used. The lower limbs of three cadavers were made to undergo movements with prevailing flexion–extension components. Femur pose was accurately measured using pin markers and the movement of twelve thigh skin markers observed relative to it. The STAs of all possible clusters of four skin markers were analysed. RM and NRM exhibited similar magnitudes and therefore impact on bone pose estimation. Thus bone pose estimators should not account for NRM only, as is normally the case, but also for RM.

Description: Wrist actigraphy (ACT) is a low-cost and well-established technique for long-term monitoring of human activity. It has a special relevance in sleep studies, where its noninvasive nature makes it a valuable tool for behavioral characterization and for the detection and diagnosis of some sleep disorders. The traditional sleep/ wakefulness state estimation algorithms from the nocturnal ACT data are unbalanced from a sensitivity and specificity points of view since they tend to overestimate sleep state, with severe consequences from a diagnosis point of view. They usually maximize the overall accuracy that does not take into account the highly unbalanced state distribution. In this paper, a method is proposed to appropriately deal with this unbalanced problem, achieving similar sensitivity and specificity scores in the state estimation process. The proposed method combines two linear discriminant classifiers, trained with two different criteria involving movement detection to generate a first state estimate. This result is then refined by a Hidden Markov Model-based algorithm. The global accuracy, the sensitivity, and the specificity of the method are $77.8 %, 75.6 %$ , and $81.6 %$ , respectively, performing better than the tested algorithms. If the performance is assessed only for movement periods, this improvement is even higher.

Description: The combination of mathematical modeling and optimal control techniques holds great potential for quantitatively describing tumor progression and optimal treatment planning. Hereby, we use a Gompertz-type growth law and a pharmacokinetic-pharmacodynamic approach for modeling the effects of drugs on tumor progression in tumor bearing mice, and we combine these in order to design optimal therapeutic patterns. Specifically, we describe colon cancer progression in both untreated mice as well as mice treated with widely used anticancer agents. We also present a pharmacokinetic model to describe the kinetics of drugs in the body as well as detailed toxicity models to describe the severity of side effects. Finally, we propose a promising methodology by which cancer progression in mice with drug resistance can be controlled. By using optimal control, we demonstrate that the optimal planning of the frequency and magnitude of treatment interruptions is key to the control of cancer progression in subjects with resistance and should be further investigated in an experimental setting, which is currently underway.

Description: While abnormal patterns of cardiac electrophysiological activation are at the origin of important cardiovascular diseases (e.g., arrhythmia, asynchrony), the only clinically available method to observe detailed left ventricular endocardial surface activation pattern is through invasive catheter mapping. However, this electrophysiological activation controls the onset of the mechanical contraction; therefore, important information about the electrophysiology could be deduced from the detailed observation of the resulting motion patterns. In this paper, we present the study of this inverse cardiac electrokinematic relationship. The objective is to predict the activation pattern knowing the cardiac motion from the analysis of cardiac image sequences. To achieve this, we propose to create a rich patient-specific database of synthetic time series of the cardiac images using simulations of a personalized cardiac electromechanical model, in order to study this complex relationship between electrical activity and kinematic patterns in the context of this specific patient. We use this database to train a machine-learning algorithm which estimates the depolarization times of each cardiac segment from global and regional kinematic descriptors based on displacements or strains and their derivatives. Finally, we use this learning to estimate the patient’s electrical activation times using the acquired clinical images. Experiments on the inverse electrokinematic learning are demonstrated on synthetic sequences and are evaluated on clinical data with promising results. The error calculated between our prediction and the invasive intracardiac mapping ground truth is relatively small (around 10 ms for ischemic patients and 20 ms for nonischemic patient). This approach suggests the possibility of noninvasive electrophysiological pattern estimation using cardiac motion imaging.

Description: Brain electrical impedance tomography (EIT) is an emerging method for monitoring brain injuries. To effectively evaluate brain EIT systems and reconstruction algorithms, we have developed a novel head phantom that features realistic anatomy and spatially varying skull resistivity. The head phantom was created with three layers, representing scalp, skull, and brain tissues. The fabrication process entailed 3-D printing of the anatomical geometry for mold creation followed by casting to ensure high geometrical precision and accuracy of the resistivity distribution. We evaluated the accuracy and stability of the phantom. Results showed that the head phantom achieved high geometric accuracy, accurate skull resistivity values, and good stability over time and in the frequency domain. Experimental impedance reconstructions performed using the head phantom and computer simulations were found to be consistent for the same perturbation object. In conclusion, this new phantom could provide a more accurate test platform for brain EIT research.

Description: Potassium-39 ( $^{39}$ K) magnetic resonance imaging (MRI) is a noninvasive technique which could potentially allow for detecting intracellular physiological variations in common human pathologies such as stroke and cancer. However, the low signal-to-noise ratio (SNR) achieved in $^{39}$ K-MR images hampered data acquisition with sufficiently high spatial and temporal resolution in animal models so far. Full wave electromagnetic (EM) simulations were performed for a single-loop copper (Cu) radio frequency (RF) surface resonator with a diameter of 30 mm optimized for rat brain imaging at room temperature (RT) and at liquid nitrogen (LN $_{2})$ with a temperature of 77 K. A novel cryogenic Cu RF surface resonator with home-built LN $_{2}$ nonmagnetic G10 fiberglass cryostat system for small animal scanner at 9.4 T was designed, built and tested in phantom and in in vivo MR measurements. Aerogel was used for thermal insulation in the developed LN $_{2}$ cryostat. In this paper, we present the first in vivo $^{39}$ K-MR images at 9.4 T for both healthy and stroke-induced rats using the developed cryogenic coil at 77 K. In good agreement with EM-simulations and bench-top measurements, the developed cryogenic coil improved the SNR by factor of 2.7 ± 0.2 in both phantom and in in vivo MR imaging compared with the same coil at RT.

Description: Atrial fibrillation (AF) electrograms are characterized by varying morphologies, amplitudes, and cycle lengths (CLs), presenting a challenge for automated detection of individual activations and the activation rate. In this study, we evaluate an algorithm to detect activations and measure CLs from AF electrograms. This algorithm iteratively adjusts the detection threshold level until the mean CL converges with the median CL to detect all individual activations. A total of 291 AF electrogram recordings from 13 patients (11 male, 58 ± 10 years old) undergoing AF ablation were obtained. Using manual markings by two independent reviewers as the standard, we compared the cycle length iteration algorithm with a fixed threshold algorithm and dominant frequency (DF) for the estimation of CL. At segment lengths of 10 s, when comparing the algorithm detected to the manually detected activation, the undersensing, oversensing, and total discrepancy rates were 2.4%, 4.6%, and 7.0%, respectively, and with absolute differences in mean and median CLs were 7.9 ± 9.6 ms and 5.6 ± 6.8 ms, respectively. These results outperformed DF and fixed threshold-based measurements. This robust method can be used for CL measurements in either real-time and offline settings and may be useful in the mapping of AF.

Description: This paper presents a new real-time automated infrared video monitoring technique for detection of breathing anomalies, and its application in the diagnosis of obstructive sleep apnea. We introduce a novel motion model to detect subtle, cyclical breathing signals from video, a new 3-D unsupervised self-adaptive breathing template to learn individuals’ normal breathing patterns online, and a robust action classification method to recognize abnormal breathing activities and limb movements. This technique avoids imposing positional constraints on the patient, allowing patients to sleep on their back or side, with or without facing the camera, fully or partially occluded by the bed clothes. Moreover, shallow and abdominal breathing patterns do not adversely affect the performance of the method, and it is insensitive to environmental settings such as infrared lighting levels and camera view angles. The experimental results show that the technique achieves high accuracy ( $hbox{94}%$ for the clinical data) in recognizing apnea episodes and body movements and is robust to various occlusion levels, body poses, body movements (i.e., minor head movement, limb movement, body rotation, and slight torso movement), and breathing behavior (e.g., shallow versus heavy breathing, mouth breathing, chest breathing, and abdominal breathing).

Description: Recently, magnetoencephalography (MEG)-based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods of neuroscience research and therapy. Artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming processing. With cardiac artifact rejection for real-time analysis (CARTA), we introduce a novel algorithm capable of real-time cardiac artifact (CA) rejection. The method is based on constrained independent component analysis (ICA), where a priori information of the underlying source signal is used to optimize and accelerate signal decomposition. In CARTA, this is performed by estimating the subject's individual density distribution of the cardiac activity, which leads to a subject-specific signal decomposition algorithm. We show that the new method is capable of effectively reducing CAs within one iteration and a time delay of 1 ms. In contrast, Infomax and Extended Infomax ICA converged not until seven iterations, while FastICA needs at least ten iterations. CARTA was tested and applied to data from three different but most common MEG systems (4-D-Neuroimaging, VSM MedTech Inc., and Elekta Neuromag). Therefore, the new method contributes to reliable signal analysis utilizing BCI approaches.

Description: Stroke is a major cause of death and disability worldwide. Therapeutic hypothermia is a potentially useful neuroprotective treatment. A mathematical model of brain metabolism during stroke is extended here to simulate the effect of hypothermia on cell survival. Temperature decreases were set to reduce chemical reaction rates and slow diffusion through ion channels according to the $Q_{10}$ rule. Heat delivery to tissues was set to depend on metabolic heat generation rate and perfusion. Two cooling methods, scalp and vascular, were simulated to approximate temperature variation in the brain during treatment. Cell death was assumed to occur at continued cell membrane depolarization. Simulations showed that hypothermia to 34.5 °C induced within 1–1.5 h of stroke onset could extend cell survival time by at least 5 h in tissue with perfusion reduced by 80% of normal. There was good agreement between simulated metabolite dynamics and those reported in rat model studies.

Description: Magnetic stimulation noninvasively modulates neuronal activity through a magnetically induced current. However, despite the usefulness and popularity of this method, the effects of neuronal activity in the nonstimulated regions on the stimulus responses are unknown. Here, we report that the induced current-evoked responses were affected by neuronal activities in the nonstimulated regions. Our experiment used a Mu-metal-based localized induced current stimulation (LICS) system combined with the microfabricated cell culture chamber system and a microelectrode array (MEA). The cell culture chamber system has radiating microtunnels connecting one central and eight outer chambers, which were fabricated using soft lithography and a replica modeling technique with SU-8 photoresist and polydimethylsiloxane (PDMS). Rat cortical neurons were separately cultured in the chambers and formed functional synaptic connections through the microtunnels. By applying a biphasic alternating pulsed magnetic field to the Mu-metal located in the central chamber, induced currents were mainly generated near the cultured neurons and modified the neuronal activities, which were recorded through MEA. Furthermore, we confirmed that the evoked responses were modified by localized pharmacological stimulation (LPS) in the outer chambers. These results suggest that our system would be promising tool for analyzing the effect of magnetic stimulation on interacting neuronal activity.

Description: Bivalirudin, used in patients with heparin-induced thrombocytopenia, is a direct thrombin inhibitor. Since it is a rarely used drug, clinical experience with its dosing is sparse. We develop two approaches to predict the Partial Thromboplastin Time (PTT) based on bivalirudin infusion rates. The first approach is model free and utilizes regularized regression. It is flexible enough to be used as predictors bivalirudin infusion rates measured over several time instances before the time at which a PTT prediction is sought. The second approach is model based and proposes a specific model for obtaining PTT which uses a shorter history of the past measurements. We learn population-wide model parameters by solving a nonlinear optimization problem. We also devise an adaptive algorithm based on the extended Kalman filter that can adapt model parameters to individual patients. The latter adaptive model emerges as the most promising as it yields reduced mean error compared to the model-free approach. The model accuracy we demonstrate on actual patient measurements is sufficient to be useful in guiding the optimal therapy.

Description: This paper proposes a new wireless biopsy method where a magnetically actuated untethered soft capsule endoscope carries and releases a large number of thermo-sensitive, untethered microgrippers (μ-grippers) at a desired location inside the stomach and retrieves them after they self-fold and grab tissue samples. We describe the working principles and analytical models for the μ-gripper release and retrieval mechanisms, and evaluate the proposed biopsy method in ex vivo experiments. This hierarchical approach combining the advanced navigation skills of centimeter-scaled untethered magnetic capsule endoscopes with highly parallel, autonomous, submillimeter scale tissue sampling μ-grippers offers a multifunctional strategy for gastrointestinal capsule biopsy.

Description: Diabetes mellitus (DM) and its complications leading to diabetic retinopathy (DR) are soon to become one of the 21st century's major health problems. This represents a huge financial burden to healthcare officials and governments. To combat this approaching epidemic, this paper proposes a noninvasive method to detect DM and nonproliferative diabetic retinopathy (NPDR), the initial stage of DR based on three groups of features extracted from tongue images. They include color, texture, and geometry. A noninvasive capture device with image correction first captures the tongue images. A tongue color gamut is established with 12 colors representing the tongue color features. The texture values of eight blocks strategically located on the tongue surface, with the additional mean of all eight blocks are used to characterize the nine tongue texture features. Finally, 13 features extracted from tongue images based on measurements, distances, areas, and their ratios represent the geometry features. Applying a combination of the 34 features, the proposed method can separate Healthy/DM tongues as well as NPDR/DM-sans NPDR (DM samples without NPDR) tongues using features from each of the three groups with average accuracies of 80.52% and 80.33%, respectively. This is on a database consisting of 130 Healthy and 296 DM samples, where 29 of those in DM are NPDR.

Description: Magnetic detection electrical impedance tomography (MDEIT) is an imaging modality that aims to compute the cross-sectional distribution of the conductivity inside a volume. The current is injected into the volume by the surface electrodes and the resulting magnetic fields surrounding the object are detected by coils. The image resolution and contrast in MDEIT image reconstruction are affected by the parameters such as the numbers and locations of electrodes and measurements, and the finite-element mesh resolution. This paper addresses the numerical experiment applied to the singular value analysis (SVA) of the sensitivity matrix in the presence of noisy measurements, subsequently suggesting the optimal electrode and detector configurations for the whole imaging object region. For the region of interest (RoI), the combined SVA and redundancy reduction is used to obtain the optimum measurement arrangement. Finally, the optimum design is confirmed by examining the image reconstructions of the simulated data acquired with different measurement arrangements. The results indicate that properly increasing the number of current injections and the number of measurement circles, and locating preferentially the electrodes and detectors on the region nearest to the RoI produce more useful singular values and better reconstructed images. These results provide guidelines for the design of the MDEIT experimental system.

Description: Stroke survivors may benefit from robotic assistance for relearning of functional movements. Current assistive devices are either passive, limited to only two dimensions or very powerful. However, for reach training, weight compensation and a little assistance with limited power is sufficient. We designed and evaluated a novel three-dimensional robotic manipulator, which is able to support the arm weight and assist functional reaching movements. Key points of the design are a damper-based drive train, giving an inherently safe system and its compact and lightweight design. The system is force actuated with a bandwidth of up to 2.3 Hz, which is sufficient for functional arm movements. Maximal assistive forces are 15 N for the up/down and forward/backward directions and 10 N for the left/right direction. Force tracking errors are smaller than 1.5 N for all axes and the total weight of the robot is 25 kg. Furthermore, the device has shown its benefit for increasing reaching distance in a single-case study with a stroke subject. The newly developed system has the technical ability to assist the arm during movement, which is a prerequisite for successful training of stroke survivors. Therapeutic effects of the applied assistance need to be further evaluated. However, with its inherent safety and ease of use, this newly developed system even has the potential for home-based therapeutic training after stroke.

Description: The Kleihauer–Betke (KB) test is the standard method for quantitating fetal-maternal hemorrhage in maternal care. In hospitals, the KB test is performed by a certified technologist to count a minimum of 2000 fetal and maternal red blood cells (RBCs) on a blood smear. Manual counting suffers from inherent inconsistency and unreliability. This paper describes a system for automated counting and distinguishing fetal and maternal RBCs on clinical KB slides. A custom-adapted hardware platform is used for KB slide scanning and image capturing. Spatial-color pixel classification with spectral clustering is proposed to separate overlapping cells. Optimal clustering number and total cell number are obtained through maximizing cluster validity index. To accurately identify fetal RBCs from maternal RBCs, multiple features including cell size, roundness, gradient, and saturation difference between cell and whole slide are used in supervised learning to generate feature vectors, to tackle cell color, shape, and contrast variations across clinical KB slides. The results show that the automated system is capable of completing the counting of over 60 000 cells (versus $sim$ 2000 by technologists) within 5 min (versus $sim$ 15 min by technologists). The throughput is improved by approximately 90 times compared to manual reading by technologists. The counting results are highly accurate and correlate strongly with those from benchmarking flow cytometry measurement.

Description: In total liquid ventilation (TLV), the lungs are filled with a breathable liquid perfluorocarbon (PFC) while a liquid ventilator ensures proper gas exchange by renewal of a tidal volume of oxygenated and temperature-controlled PFC. Given the rapid changes in core body temperature generated by TLV using the lung has a heat exchanger, it is crucial to have accurate and reliable core body temperature monitoring and control. This study presents the design of a virtual lung temperature sensor to control core temperature. In the first step, the virtual sensor, using expired PFC to estimate lung temperature noninvasively, was validated both in vitro and in vivo . The virtual lung temperature was then used to rapidly and automatically control core temperature. Experimentations were performed using the Inolivent-5.0 liquid ventilator with a feedback controller to modulate inspired PFC temperature thereby controlling lung temperature. The in vivo experimental protocol was conducted on seven newborn lambs instrumented with temperature sensors at the femoral artery, pulmonary artery, oesophagus, right ear drum, and rectum. After stabilization in conventional mechanical ventilation, TLV was initiated with fast hypothermia induction, followed by slow posthypothermic rewarming for 1 h, then by fast rewarming to normothermia and finally a second fast hypothermia induction phase. Results showed that the virtual lung temperature was able to provide an accurate estimation of systemic arterial temperature. Results also demonstrate that TLV can precisely control core body temperature and can be favorably compared to extracorporeal circulation in terms of speed.

Description: We present a new method for measuring photoplethysmogram signals remotely using ambient light and a digital camera that allows for accurate recovery of the waveform morphology (from a distance of 3 m). In particular, we show that the peak-to-peak time between the systolic peak and diastolic peak/inflection can be automatically recovered using the second-order derivative of the remotely measured waveform. We compare measurements from the face with those captured using a contact fingertip sensor and show high agreement in peak and interval timings. Furthermore, we show that results can be significantly improved using orange, green, and cyan color channels compared to the tradition red, green, and blue channel combination. The absolute error in interbeat intervals was 26 ms and the absolute error in mean systolic–diastolic peak-to-peak times was 12 ms. The mean systolic–diastolic peak-to-peak times measured using the contact sensor and the camera were highly correlated, $rho$ = 0.94 ( p $〈$ 0.001). The results were obtained with a camera frame-rate of only 30 Hz. This technology has significant potential for advancing healthcare.

Description: Open to all EMBS members, our mentor program provides students and young professionals interested in the biomedical engineering profession with direct access to experienced mentors who can share valuable career guidance and advice, and contribute to professional and personal development. The program also provides experienced professionals with the opportunity to share their knowledge and inspire, encourage, and support future biomedical engineers. The mentor program is a valuable part of your membership in EMBS. Visit www.embs.chronus.com to get started. There, you will find all the information you'll need, along with frequently asked questions for both mentees and mentors.

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Description: Teleoperated robot-assisted surgery (RAS) is used to perform a wide variety of minimally invasive procedures. However, current understanding of the effect of robotic manipulation on the motor coordination of surgeons is limited. Recent studies in human motor control suggest that we optimize hand movement stability and task performance while minimizing control effort and improving robustness to unpredicted disturbances. To achieve this, the variability of joint angles and muscle activations is structured to reduce task-relevant variability and increase task-irrelevant variability. In this study, we determine whether teleoperation of a da Vinci Si surgical system in a nonclinical task of simple planar movements changes this structure of variability in experienced surgeons and novices. To answer this question, we employ the UnControlled manifold analysis that partitions users’ joint angle variability into task-irrelevant and task-relevant manifolds. We show that experienced surgeons coordinate their joint angles to stabilize hand movements more than novices, and that the effect of teleoperation depends on experience—experts increase teleoperated stabilization relative to freehand whereas novices decrease it. We suggest that examining users’ exploitation of the task-irrelevant manifold for stabilization of hand movements may be applied to: (1) evaluation and optimization of teleoperator design and control parameters, and (2) skill assessment and optimization of training in RAS.

Description: The use of irreversible electroporation (IRE) for cancer treatment has increased sharply over the past decade. As a nonthermal therapy, IRE offers several potential benefits over other focal therapies, which include 1) short treatment delivery time, 2) reduced collateral thermal injury, and 3) the ability to treat tumors adjacent to major blood vessels. These advantages have stimulated widespread interest in basic through clinical studies of IRE. For instance, many in vitro and in vivo studies now identify treatment planning protocols (IRE threshold, pulse parameters, etc.), electrode delivery (electrode design, placement, intraoperative imaging methods, etc.), injury evaluation (methods and timing), and treatment efficacy in different cancer models. Therefore, this study reviews the in vitro , translational, and clinical studies of IRE cancer therapy based on major experimental studies particularly within the past decade. Further, this study provides organized data and facts to assist further research, optimization, and clinical applications of IRE.

Description: Cell population represents an intrinsically heterogeneous and stochastic system, in which individual cells often behave very differently in molecular contents, functions and even genotypes from the population average in response to uniform physiological stimuli. The traditional bulk cellular analysis often overlooks cellular heterogeneity and does not provide information on cell–cell variations. Single-cell measurements can reveal information obscured in population averages, and enable us to determine distributions rather than averaged properties within a cell population. The level of complexity, with numerous variables acting at the same time, requires multiparametric and dynamic investigation of a large number of single cells. Multiplexed study can provide quantitative correlations or inter-relationships among multiple cellular components and molecular markers within a protein network or family in biological processes. In this paper, we applied multiple fluorophore-conjugated primary antibodies to detect multiple proteins expressed on the same singe cells from a clonal population. To reveal cell–cell heterogeneity, we quantified the histograms of six proteins within a cell population as functions of TNF-α stimulation time. Then, we quantified noise and noise strength of these protein histograms as functions of TNF-α stimulation time. Thirdly, we quantified correlation coefficients of multiple proteins expressed on same single-cells as functions of TNF-α stimulation time. Above parameters demonstrated nonlinear relationships with TNF-α stimulation. Quantification of above parameters on independent cell subpopulations further reveals the cell–cell heterogeneity when exposed to identical environmental conditions. Such cellular heterogeneity will be useful to characterize the disease progression and disease diagnoses.

Description: Capacitive electrodes are a promising alternative to the conventional adhesive electrodes for ECG measurements. They provide more comfort to the patient when integrated in everyday objects (e.g., beds or seats) for long-term monitoring. However, the application of capacitive sensors is limited by their high sensitivity to motion artifacts. For example, motion at the body–electrode interface causes variations of the coupling capacitance which, in the presence of a dc voltage across the coupling capacitor, create strong artifacts in the measurements. The origin, relevance, and reduction of this specific and important type of artifacts are studied here. An injection signal is exploited to track the variations of the coupling capacitance in real time. This information is then used by an identification scheme to estimate the artifacts and subtract them from the measurements. The method was evaluated in simulations, lab environments, and in a real-life recording on an adult's chest. For the type of artifact under study, a strong artifact reduction ranging from 40 dB for simulated data to 9 dB for a given real-life recording was achieved. The proposed method is automated, does not require any knowledge about the measurement system parameters, and provides an online estimate for the dc voltage across the coupling capacitor.

Description: The Hilbert transform has been used to characterize wave propagation and detect phase singularities during cardiac fibrillation. Two mapping modalities have been used: optical mapping (used to map atria and ventricles) and contact electrode mapping (used only to map ventricles). Due to specific morphology of atrial electrograms, phase reconstruction of contact electrograms in the atria is challenging and has not been investigated in detail. Here, we explore the properties of Hilbert transform applied to unipolar epicardial electrograms and devise a method for robust phase reconstruction using the Hilbert transform. We applied the Hilbert transform to idealized unipolar signals obtained from analytical approach and to electrograms recorded in humans. We investigated effects of deflection morphology on instantaneous phase. Application of the Hilbert transform to unipolar electrograms demonstrated sensitivity of reconstructed phase to the type of deflection morphology (uni- or biphasic), the ratio of R and S waves and presence of the noise. In order to perform a robust phase reconstruction, we propose a signal transformation based on the recomposition of the electrogram from sinusoidal wavelets with amplitudes proportional to the negative slope of the electrogram. Application of the sinusoidal recomposition transformation prior to application of the Hilbert transform alleviates the effect of confounding features on reconstructed phase.

Description: Water permeability of the plasma membrane plays an important role in making optimal cryopreservation protocols for different types of cells. To quantify water permeability effectively, automated cell volume segmentation during freezing is necessary. Unfortunately, there exists so far no efficient and accurate segmentation method to handle this kind of image processing task gracefully. The existence of extracellular ice and variable background present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel approach to reliably extract cells from the extracellular ice, which attaches to or surrounds cells. Our method operates on temporal image sequences and is composed of two steps. First, for each image from the sequence, a greedy search strategy is employed to track approximate locations of cells in motion. Second, we utilize a localized competitive active contour model to obtain the contour of each cell. Based on the first step's result, the initial contour for level set evolution can be determined appropriately, thus considerably easing the pain of initialization for an active contour model. Experimental results demonstrate that the proposed method is efficient and effective in segmenting cells during freezing.

Description: Electrocardiogram (ECG) monitoring is a critical tool in patient care, but its utility is often balanced with frustration from clinicians who are constantly distracted by false alarms. This has motivated the need to readdress the major factors that contribute to ECG noise with the goal of reducing false alarms. In this study, we describe a previously unreported phenomenon in which ECG noise can result from an unintended interaction between two systems: 1) the dc lead-off circuitry that is used to detect whether electrodes fall off the patient; and 2) the right-leg drive (RLD) system that is responsible for reducing ac common-mode noise that couples into the body. Using a circuit model to study this interaction, we found that in the presence of a dc lead-off system, even moderate increases in the right-leg skin–electrode resistance can cause the RLD amplifier to saturate. Such saturation can produce ECG noise because the RLD amplifier will no longer be capable of attenuating ac common-mode noise on the body. RLD saturation is particularly a problem for modern ECG monitors that use low-voltage supply levels. For example, for a 12-lead ECG and a 2 V power supply, saturation will occur when the right-leg electrode resistance reaches only 2 MΩ. We discuss several design solutions that can be used in low-voltage monitors to avoid RLD saturation.

Description: Gaussian process (GP) models are a flexible means of performing nonparametric Bayesian regression. However, GP models in healthcare are often only used to model a single univariate output time series, denoted as single-task GPs (STGP). Due to an increasing prevalence of sensors in healthcare settings, there is an urgent need for robust multivariate time-series tools. Here, we propose a method using multitask GPs (MTGPs) which can model multiple correlated multivariate physiological time series simultaneously. The flexible MTGP framework can learn the correlation between multiple signals even though they might be sampled at different frequencies and have training sets available for different intervals. Furthermore, prior knowledge of any relationship between the time series such as delays and temporal behavior can be easily integrated. A novel normalization is proposed to allow interpretation of the various hyperparameters used in the MTGP. We investigate MTGPs for physiological monitoring with synthetic data sets and two real-world problems from the field of patient monitoring and radiotherapy. The results are compared with standard Gaussian processes and other existing methods in the respective biomedical application areas. In both cases, we show that our framework learned the correlation between physiological time series efficiently, outperforming the existing state of the art.

Description: High performance of the brain–computer interface (BCI) needs efficient algorithms to extract discriminative features from raw electroencephalography (EEG) signals. In this paper, we present a novel scheme to extract spatial spectral features for the motor imagery-based BCI. The learning task is formulated by maximizing the mutual information between spatial spectral features (MMISS) and class labels, by which a unique objective function directly related to Bayes classification error is optimized. The spatial spectral features are assumed to follow a parametric Gaussian distribution, which has been validated by the normal distribution Mardia's test, and under this assumption the estimation of mutual information is derived. We propose a gradient based alternative and iterative learning algorithm to optimize the cost function and derive the spatial and spectral filters simultaneously. The experimental results on dataset IVa of BCI competition III and dataset IIa of BCI competition IV show that the proposed MMISS is able to efficiently extract discriminative features from motor imagery-based EEG signals to enhance the classification accuracy compared to other existing algorithms.

Description: Compared to robotic injection of suspended cells (e.g., embryos and oocytes), fewer attempts were made to automate the injection of adherent cells (e.g., cancer cells and cardiomyocytes) due to their smaller size, highly irregular morphology, small thickness (a few micrometers thick), and large variations in thickness across cells. This paper presents a robotic system for automated microinjection of adherent cells. The system is embedded with several new capabilities: automatically locating micropipette tips; robustly detecting the contact of micropipette tip with cell culturing surface and directly with cell membrane; and precisely compensating for accumulative positioning errors. These new capabilities make it practical to perform adherent cell microinjection truly via computer mouse clicking in front of a computer monitor, on hundreds and thousands of cells per experiment (versus a few to tens of cells as state of the art). System operation speed, success rate, and cell viability rate were quantitatively evaluated based on robotic microinjection of over 4000 cells. This paper also reports the use of the new robotic system to perform cell–cell communication studies using large sample sizes. The gap junction function in a cardiac muscle cell line (HL-1 cells), for the first time, was quantified with the system.

Description: Mesoscopic fluorescence molecular tomography (MFMT) is new imaging modality aiming at 3-D imaging of molecular probes in a few millimeter thick biological samples with high-spatial resolution. In this paper, we develop a compressive sensing-based reconstruction method with $l_{1}$ -norm regularization for MFMT with the goal of improving spatial resolution and stability of the optical inverse problem. Three-dimensional numerical simulations of anatomically accurate microvasculature and real data obtained from phantom experiments are employed to evaluate the merits of the proposed method. Experimental results show that the proposed method can achieve 80 μm spatial resolution for a biological sample of 3 mm thickness and more accurate quantifications of concentrations and locations for the fluorophore distribution than those of the conventional methods.

Description: High-resolution (HR) mapping employs multielectrode arrays to achieve spatially detailed analyses of propagating bioelectrical events. A major current limitation is that spatial analyses must currently be performed “off-line” (after experiments), compromising timely recording feedback and restricting experimental interventions. These problems motivated development of a system and method for “online” HR mapping. HR gastric recordings were acquired and streamed to a novel software client. Algorithms were devised to filter data, identify slow-wave events, eliminate corrupt channels, and cluster activation events. A graphical user interface animated data and plotted electrograms and maps. Results were compared against off-line methods. The online system analyzed 256-channel serosal recordings with no unexpected system terminations with a mean delay 18 s. Activation time marking sensitivity was 0.92; positive predictive value was 0.93. Abnormal slow-wave patterns including conduction blocks, ectopic pacemaking, and colliding wave fronts were reliably identified. Compared to traditional analysis methods, online mapping had comparable results with equivalent coverage of 90% of electrodes, average RMS errors of less than 1 s, and CC of activation maps of 0.99. Accurate slow-wave mapping was achieved in near real-time, enabling monitoring of recording quality and experimental interventions targeted to dysrhythmic onset. This work also advances the translation of HR mapping toward real-time clinical application.

Description: Needle insertions underlie a diversity of medical interventions. Steerable needles provide a means by which to enhance existing needle-based interventions and facilitate new ones. Tip-steerable needles follow a curved path and can be steered by twisting the needle base during insertion, but this twisting excites torsional dynamics that introduce a discrepancy between the base and tip twist angles. Here, we model the torsional dynamics of a flexible rod—such as a tip-steerable needle—during subsurface insertion and develop a new controller based on the model. The torsional model incorporates time-varying mode shapes to capture the changing boundary conditions inherent during insertion. Numerical simulations and physical experiments using two distinct setups—stereo camera feedback in semitransparent artificial tissue and feedback control with real-time X-ray imaging in optically opaque artificial tissue—demonstrate the need to account for torsional dynamics in control of the needle tip.

Description: Ultrasound (US)-based thermal imaging is very sensitive to tissue motion, which is a major obstacle to apply US temperature monitoring to noninvasive thermal therapies of in vivo subjects. In this study, we aim to develop a motion compensation method for stable US thermal imaging in in vivo subjects. Based on the assumption that the major tissue motion is approximately periodic caused by respiration, we propose a motion compensation method for change in backscattered energy (CBE) with multiple reference frames. Among the reference frames, the most similar reference to the current frame is selected to subtract the respiratory-induced motions. Since exhaustive reference searching in all stored reference frames can impede real-time thermal imaging, we improve the reference searching by using a motion-mapped reference model. We tested our method in six tumor-bearing mice with high intensity focused ultrasound (HIFU) sonication in the tumor volume until the temperature had increased by $7,^{circ }{rm C}$ . The proposed motion compensation was evaluated by root-mean-square-error (RMSE) analysis between the estimated temperature by CBE and the measured temperature by thermocouple. As a result, the mean $pm$ SD RMSE in the heating range was $1.1pm 0.1,^{circ }{rm C}$ with the proposed method, while the corresponding result without motion compensation was $4.3pm 2.6,^{circ }{rm C}$ . In addition, with the idea of motion-mapped reference frame, total processing time to produce a frame of thermal image was reduced in comparison with the exhaustive reference searching, which enabled the motion-compensated thermal imaging in 15 frames per second with 150 reference frames under 50 $%$ HIFU duty ratio.

Description: Thermal ablation by using electromagnetic thermotherapy (EMT) has been a promising cancer modality in recent years. It has relatively few side effects and has therefore been extensively investigated for a variety of medical applications in internal medicine and surgery. The EMT system applies a high-frequency alternating electromagnetic field to heat up the needles which are inserted into the target tumor to cause tumor ablation. In this study, a new synchronized-coil EMT system was demonstrated, which was equipped with two synchronized coils and magnetic field generators to provide a long-range, penetrated electromagnetic field to effectively heat up the needles. The heating effect of the needles at the center of the two coils was first explored. The newly designed two-section needle array combined with the synchronized-coil EMT system was thus demonstrated in the in vitro and in vivo animal experiments. Experimental data showed that the developed system is promising for minimally invasive surgery since it might provide superior performance for thermotherapy in cancer treatment.

Description: Atomic decomposition (AD) can be used to efficiently decompose an arbitrary signal. In this paper, we present a method to detect neonatal electroencephalogram (EEG) seizure based on AD via orthogonal matching pursuit using a novel, application-specific, dictionary. The dictionary consists of pseudoperiodic Duffing oscillator atoms which are designed to be coherent with the seizure epochs. The relative structural complexity (a measure of the rate of convergence of AD) is used as the sole feature for seizure detection. The proposed feature was tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The seizure detection system using the proposed dictionary was able to achieve a median receiver operator characteristic area of 0.91 (IQR 0.87–0.95) across 18 neonates.

Description: This paper presents a powered finger exoskeleton with an open fingerpad, named the Open Fingerpad eXoskeleton (OFX). The palmar opening at the fingerpad allows for direct contact between the user’s fingerpad and objects in order to make use of the wearer’s own tactile sensation for dexterous manipulation. Lateral side walls at the end of the OFX’s index finger module are equipped with custom load cells for estimating the wearer’s pinch grip force. A pneumatic cylinder generates assistance force, which is determined according to the estimated pinch grip force. The OFX transmits the assistance force directly to the objects without exerting pressure on the wearer’s finger. The advantage of the OFX over an exoskeleton with a closed fingerpad was validated experimentally. During static and dynamic manipulation of a test object, the OFX exhibited a lower safety margin than the closed exoskeleton, indicating a higher ability to adjust the grip force within an appropriate range. Furthermore, the benefit of force assistance in reducing the muscular burden was observed in terms of muscle fatigue during a static pinch grip. The median frequency (MDF) of the surface electromyography (sEMG) signal from the first dorsal interosseous (FDI) muscle displayed a lower reduction rate for the assisted condition, indicating a lower accumulation rate of muscle fatigue.

Description: Computer-assisted training systems promote both training efficacy and patient health. An important component for providing automatic feedback in computer-assisted training systems is workflow segmentation: the determination of what task in the workflow is being performed. Our objective was to develop a workflow segmentation algorithm for needle interventions using needle tracking data. Needle tracking data were collected from ultrasound-guided epidural injections and lumbar punctures, performed by medical personnel. The workflow segmentation algorithm was tested in a simulated real-time scenario: the algorithm was only allowed access to data recorded at, or prior to, the time being segmented. Segmentation output was compared to the ground-truth segmentations produced by independent blinded observers. Overall, the algorithm was 93% accurate. It automatically segmented the ultrasound-guided epidural procedures with 81% accuracy and the lumbar punctures with 82% accuracy. Given that the manual segmentation consistency was only 84%, the algorithm's accuracy was 93%. Using Cohen's d statistic, a medium effect size (0.5) was calculated. Because the algorithm segments needle-based procedures with such high accuracy, expert observers can be augmented by this algorithm without a large decrease in ability to follow trainees in a workflow. The proposed algorithm is feasible for use in a computer-assisted needle placement training system.

Description: The major aim of our study is to demonstrate that a concerted combination of time-variant, frequency-selective, linear and nonlinear analysis approaches can be beneficially used for the analysis of heart rate variability (HRV) in epileptic patients to reveal premonitory information regarding an imminent seizure and to provide more information on the mechanisms leading to changes of the autonomic nervous system. The quest is to demonstrate that the combined approach gains new insights into specific short-term patterns in HRV during preictal, ictal, and postictal periods in epileptic children. The continuous Morlet-wavelet transform was used to explore the time-frequency characteristics of the HRV using spectrogram, phase-locking, band-power and quadratic phase coupling analyses. These results are completed by time-variant characteristics derived from a signal-adaptive approach. Advanced empirical mode decomposition was utilized to separate out certain HRV components, in particular blood-pressure-related Mayer waves (≈0.1 Hz) and respiratory sinus arrhythmia (≈0.3 Hz). Their time-variant nonlinear predictability was analyzed using local estimations of the largest Lyapunov exponent (point prediction error). Approximately 80–100 s before the seizure onset timing and coordination of both HRV components can be observed. A higher degree of synchronization is found and with it a higher predictability of the HRV. All investigated linear and nonlinear analyses contribute with a specific importance to these results.

Description: In magnetic resonance imaging and spectroscopy, a highly uniform magnetic field is desired for minimizing image distortions and line broadening, respectively. Typically, shim coils are employed to provide correcting fields for inhomogeneities brought about by magnetic interactions with the sample under study. Flexible field modeling is possible using an array of regularly shaped conducting loops that are independently electrically driven. In this paper, a design method is presented for generating coil winding patterns for shim arrays with irregular geometry elements. These designs are compared theoretically to the use of circular loop arrays and are shown to provide considerable improvements in field accuracy and efficiency for generating low-order correcting fields.

Description: Provides a listing of current staff, committee members and society officers.

Description: X-ray luminescence computed tomography (XLCT) opens new possibilities to perform molecular imaging with X-ray. However, challenges remain in dynamic XLCT imaging, where short scan time, good spatial resolution, and whole-body field of view should be considered simultaneously. In this paper, by the use of a single-view XLCT reconstruction method based on a compressive sensing (CS) technique, incorporating a cone beam XLCT imaging system, we implement fast 3-D XLCT imaging. To evaluate the performance of the method, two types of phantom experiments were performed based on a cone beam XLCT imaging system. In Case 1, one tube filled with the X-ray-excitable nanophosphor (Gd $_{2}$ O $_{3}$ :Eu $^{3+}$ ) was immerged in different positions in the phantom to evaluate the effect of the source position on single-view XLCT reconstruction accuracy. In Case 2, two tubes filled with Gd $_{2}$ O $_{3}$ :Eu $^{3+}$ were immerged in different heights in the phantom to evaluate the whole-body imaging performance of single-view XLCT reconstruction. The experimental results indicated that the tubes used in previous phantom experiments can be resolved from single-view XCLT reconstruction images. The location error is less than 1.2 mm. In addition, since only one view data are needed to implement 3-D XLCT imaging, the acquisition time can be greatly reduced (∼1 frame/s) compared with previous XLCT systems. Hence, the technique is suited for imaging the fast distribution of the X-ray-excitable nanophosphors within a biological object.

Description: Oscillatory phenomena abound in many types of signals. Identifying the individual oscillatory components that constitute an observed biological signal leads to profound understanding about the biological system. The instantaneous frequency (IF), the amplitude modulation (AM), and their temporal variability are widely used to describe these oscillatory phenomena. In addition, the shape of the oscillatory pattern, repeated in time for an oscillatory component, is also an important characteristic that can be parametrized appropriately. These parameters can be viewed as phenomenological surrogates for the hidden dynamics of the biological system. To estimate jointly the IF, AM, and shape, this paper applies a novel and robust time-frequency analysis tool, referred to as the synchrosqueezing transform (SST). The usefulness of the model and SST are shown directly in predicting the clinical outcome of ventilator weaning. Compared with traditional respiration parameters, the breath-to-breath variability has been reported to be a better predictor of the outcome of the weaning procedure. So far, however, all these indices normally require at least $hbox{20}$ min of data acquisition to ensure predictive power. Moreover, the robustness of these indices to the inevitable noise is rarely discussed. We find that based on the proposed model, SST and only $hbox{3}$ min of respiration data, the ROC area under curve of the prediction accuracy is $hbox{0.76}$ . The high predictive power that is achieved in the weaning problem, despite a shorter evaluation period, and the stability to noise suggest that other similar kinds of signal may likewise benefit from the proposed model and SST.

Description: Box trainers equipped with sensors may help in acquiring objective information about a trainee's performance while performing training tasks with real instruments. The main aim of this study is to investigate the added value of force parameters with respect to commonly used motion and time parameters such as path length, motion volume, and task time. Two new dynamic bimanual positioning tasks were developed that not only requiring adequate motion control but also appropriate force control successful completion. Force and motion data for these tasks were studied for three groups of participants with different experience levels in laparoscopy (i.e., 11 novices, 19 intermediates, and 12 experts). In total, 10 of the 13 parameters showed a significant difference between groups. When the data from the significant motion, time, and force parameters are used for classification, it is possible to identify the skills level of the participants with 100% accuracy. Furthermore, the force parameters of many individuals in the intermediate group exceeded the maximum values in the novice and expert group. The relatively high forces used by the intermediates argue for the inclusion of training and assessment of force application during tissue handling in future laparoscopic skills training programs.

Description: Improvements in cerebrospinal fluid (CSF) draining techniques for treatment of hydrocephalus are urgently sought after to substitute for current CSF shunts that are plagued by high failure rates. The passive check valve aims to restore near natural CSF draining operations while mitigating possible failure mechanisms caused by finite leakage or low resilience that frequently constrain practical implementation of miniaturized valves. A simple hydrogel diaphragm structures core passive valve operations and enforce valve sealing properties to substantially lower reverse flow leakage. Experimental measurements demonstrate realization of targeted cracking pressures $(P_{T}$ ≈ 20–110 mmH $_{2}$ O) and operation at –800 〈 ΔP 〈 600 mmH $_{2}$ O without observable degradation or leakage.

Description: We propose a fully automated method for prostate segmentation using random forests (RFs) and graph cuts. A volume of interest (VOI) is automatically selected using supervoxel segmentation, and its subsequent classification using image features and RF classifiers. The VOIs probability map is generated using image and context features, and a second set of RF classifiers. The negative log-likelihood of the probability maps acts as the penalty cost in a second-order Markov random field cost function. Semantic information from the second set of RF classifiers is an important measure of each feature to the classification task, which contributes to formulating the smoothness cost. The cost function is optimized using graph cuts to get the final segmentation of the prostate. With average dice metric (DM) $>,0.91$ (on the training set) and DM $>,0.81$ (on the test set), our experimental results show that inclusion of the context and semantic information contributes to higher segmentation accuracy than other methods.

Description: The design and development of a functional integrated system for gastroscopy is reported in this paper. The device takes advantage of four propellers enabling locomotion in a liquid environment and generating a maximum propulsive force of 25.5 mN. The capsule has been equipped with a miniaturized wireless vision system that acquires images with a frame rate of 30 fps (frames per second). The overall size of the capsule is 32 mm in length and 22 mm in diameter, with the possibility of decreasing the diameter to swallowable dimensions. The capsule is remotely controlled by the user who can intuitively drive the device by looking at the video streaming on the graphical interface. The average speed of the device is 1.5 cm/s that allows for a fine control of the capsule motion as demonstrated in experimental tasks consisting of passing through circular targets. The video system performances have been characterized by evaluating the contrast, the focus, and the capability of acquiring and perceiving different colors. The usability of the device has been tested on bench and on explanted tissues by three users in real time target-identification tasks, in order to assess the success of the integration process. The lifetime of the capsule with active motors and vision system is 13 min, that is, a timeframe consistent with traditional gastroscopic examinations.

Description: Advanced medical imaging technologies provide a wealth of information on cardiac anatomy and structure at a paracellular resolution, allowing to identify microstructural discontinuities which disrupt the intracellular matrix. Current state-of-the-art computer models built upon such datasets account for increasingly finer anatomical details, however, structural discontinuities at the paracellular level are typically discarded in the model generation process, owing to the significant costs which incur when using high resolutions for explicit representation. In this study, a novel discontinuous finite element (dFE) approach for discretizing the bidomain equations is presented, which accounts for fine-scale structures in a computer model without the need to increase spatial resolution. In the dFE method, this is achieved by imposing infinitely thin lines of electrical insulation along edges of finite elements which approximate the geometry of discontinuities in the intracellular matrix. Simulation results demonstrate that the dFE approach accounts for effects induced by microscopic size scale discontinuities, such as the formation of microscopic virtual electrodes, with vast computational savings as compared to high resolution continuous finite element models. Moreover, the method can be implemented in any standard continuous finite element code with minor effort.

Description: A constrained weighted recursive least squares method is proposed to provide recursive models with guaranteed stability and better performance than models based on regular identification methods in predicting the variations of blood glucose concentration in patients with Type 1 Diabetes. Use of physiological information from a sports armband improves glucose concentration prediction and enables earlier recognition of the effects of physical activity on glucose concentration. Generalized predictive controllers (GPC) based on these recursive models are developed. The performance of GPC for artificial pancreas systems is illustrated by simulations with UVa-Padova simulator and clinical studies. The controllers developed are good candidates for artificial pancreas systems with no announcements from patients.

Description: Ki-67 proliferation index is a valid and important biomarker to gauge neuroendocrine tumor (NET) cell progression within the gastrointestinal tract and pancreas. Automatic Ki-67 assessment is very challenging due to complex variations of cell characteristics. In this paper, we propose an integrated learning-based framework for accurate automatic Ki-67 counting for NET. The main contributions of our method are: 1) A robust cell counting and boundary delineation algorithm that is designed to localize both tumor and nontumor cells. 2) A novel online sparse dictionary learning method to select a set of representative training samples. 3) An automated framework that is used to differentiate tumor from nontumor cells (such as lymphocytes) and immunopositive from immunonegative tumor cells for the assessment of Ki-67 proliferation index. The proposed method has been extensively tested using 46 NET cases. The performance is compared with pathologists’ manual annotations. The automatic Ki-67 counting is quite accurate compared with pathologists’ manual annotations. This is much more accurate than existing methods.

Description: While neuroimaging data can provide valuable phenotypic information to inform genetic studies, the opposite is also true: known genotypes can be used to inform brain connectivity patterns from fMRI data. Here, we propose a framework for genetically informed group brain connectivity modeling. Subjects are first stratified according to their genotypes, and then a group regularized regression model is employed for brain connectivity modeling utilizing the time courses from a priori specified regions of interest (ROIs). With such an approach, each ROI time course is in turn predicted from all other ROI time courses at zero lag using a group regression framework which also incorporates a penalty based on genotypic similarity. Simulations supported such an approach when, as previously studies have indicated to be the case, genetic influences impart connectivity differences across subjects. The proposed method was applied to resting state fMRI data from Schizophrenia and normal control subjects. Genotypes were based on D-amino acid oxidase activator (DAOA) single-nucleotide polymorphisms (SNPs) information. With DAOA SNPs information integrated, the proposed approach was able to more accurately model the diversity in connectivity patterns. Specifically, connectivity with the left putamen, right posterior cingulate, and left middle frontal gyri were found to be jointly modulated by DAOA genotypes and the presence of Schizophrenia. We conclude that the proposed framework represents a multimodal analysis approach for incorporating genotypic variability into brain connectivity analysis directly.

Description: An important problem in the study of cancer is the understanding of the heterogeneous nature of the cell population. The clonal evolution of the tumor cells results in the tumors being composed of multiple subpopulations. Each subpopulation reacts differently to any given therapy. This calls for the development of novel (regulatory network) models, which can accommodate heterogeneity in cancerous tissues. In this paper, we present a new approach to model heterogeneity in cancer. We model heterogeneity as an ensemble of deterministic Boolean networks based on prior pathway knowledge. We develop the model considering the use of qPCR data. By observing gene expressions when the tissue is subjected to various stimuli, the compositional breakup of the tissue under study can be determined. We demonstrate the viability of this approach by using our model on synthetic data, and real-world data collected from fibroblasts.

Description: This paper describes the synthesis of a 9-mers-long peptide ladder structure of glycine on a modified glass surface using a nanoliter droplet ejector. To synthesize peptide on a glass substrate, SPOT peptide synthesis protocol was followed with a nozzleless acoustic droplet ejector being used to eject about 300 droplets of preactivated amino acid solution to dispense 60 nL of the solution per mer. The coupling efficiency of each mer was measured with FITC fluorescent tag to be 96%, resulting in net 70% efficiency for the whole 9-mer-long peptide of glycine. Usage of a nanoliter droplet ejector for SPOT peptide synthesis increases the density of protein array on a chip.

Description: Deep brain stimulation effectively alleviates motor symptoms of medically refractory Parkinson’s disease, and also relieves many other treatment–resistant movement and affective disorders. Despite its relative success as a treatment option, the basis of its efficacy remains elusive. In Parkinson’s disease, increased functional connectivity and oscillatory activity occur within the basal ganglia as a result of dopamine loss. A correlative relationship between pathological oscillatory activity and the motor symptoms of the disease, in particular bradykinesia, rigidity, and tremor, has been established. Suppression of the oscillations by either dopamine replacement or DBS also correlates with an improvement in motor symptoms. DBS parameters are currently chosen empirically using a “trial and error” approach, which can be time-consuming and costly. The work presented here amalgamates concepts from theories of neural network modeling with nonlinear control engineering to describe and analyze a model of synchronous neural activity and applied stimulation. A theoretical expression for the optimum stimulation parameters necessary to suppress oscillations is derived. The effect of changing stimulation parameters (amplitude and pulse duration) on induced oscillations is studied in the model. Increasing either stimulation pulse duration or amplitude enhanced the level of suppression. The predicted parameters were found to agree well with clinical measurements reported in the literature for individual patients. It is anticipated that the simplified model described may facilitate the development of protocols to aid optimum stimulation parameter choice on a patient by patient basis.

Description: Images of pharmacokinetic parameters in dynamic fluorescence molecular tomography (FMT) have the potential to provide quantitative physiological information for biological studies and drug development. However, images obtained with conventional indirect methods suffer from low signal-to-noise ratio because of failure in efficiently modeling the measurement noise. Besides, FMT suffers from low spatial resolution due to its ill-posed nature, which further reduces the image quality. In this letter, we present a direct method with structural priors for imaging pharmacokinetic parameters, which uses a nonlinear objective function to efficiently model the measurement noise and utilizes the structural priors to mitigate the ill-posedness of FMT. The results of numerical simulations and in vivo mouse experiments demonstrate that the proposed method leads to significant improvements in the image quality.

Description: X-ray fluorescence computed tomography (XFCT) and K-edge computed tomography (CT) are two important modalities to quantify a distribution of gold nanoparticles (GNPs) in a small animal for preclinical studies. It is valuable to determine which modality is more efficient for a given application. In this paper, we report a theoretical analysis in terms of signal-to-noise ratio (SNR) for the two modalities, showing that there is a threshold of GNPs concentration such that XFCT has a better SNR than K-edge CT when GNPs concentration is less than this threshold, vice versa. Numerical tests are performed for XFCT and K-edge CT on two kinds of phantoms with multiple concentration levels and structural features. Experimental results illustrate that XFCT is superior to K-edge CT when contrast concentration is lower than 0.4% which coincides with the theoretical analysis.

Description: Nitric oxide (NO) plays an important physiological role in normal and pathological retinas. Intraretinal NO concentrations have not been directly measured due to lack of NO electrodes capable of determining their location in the retina. The microelectrodes described here allow recording of the intraretinal electroretinogram (ERG) and NO concentration from the same location, with ERGs used to determine retinal depth. Double-barreled electrodes were constructed with one barrel serving as a reference/voltage recording barrel and the other containing a Nafion-coated carbon fiber used to detect NO amperometrically. Nafion coating imparted a high selectivity for NO versus ascorbic acid (2000:1). In vivo rodent experiments demonstrated that the electrodes could record intraretinal ERGs and NO current with minimal retinal thickness deformation (9%), allowing for retinal NO depth profile measurements. Comparison of NO depth profiles under control conditions and under nitric oxide synthase (NOS) inhibition by 5 mM L-NG-Nitroarginine methyl ester (L-NAME) verified that the recorded current was attributable to NO. NO concentrations from control profiles ( $n$ = 4) were 2.37 ± 0.34 μM at the choroid and 1.12 ± 0.14 μM at the retinal surface. NO concentrations from L-NAME profiles ( $n$ = 4) were significantly lower at 0.83 ± 0.15 μM at the choroid ( $p = 0.006$ ) and 0.27 ± 0.04 μM at the retinal surface ( $p = 0.001$ ). Localized regions of increased NO (100–400 nM) were seen in the inner retina under control conditions but not after L-NAME. The dual ERG-NO electrode may be a valuable tool in eva- uating the role of NO in normal and diseased retinas.

Description: With the development of multidetector computed-tomography (MDCT) scanners and ultrathin bronchoscopes, the use of bronchoscopy for diagnosing peripheral lung-cancer nodules is becoming a viable option. The work flow for assessing lung cancer consists of two phases: 1) 3-D MDCT analysis and 2) live bronchoscopy. Unfortunately, the yield rates for peripheral bronchoscopy have been reported to be as low as 14%, and bronchoscopy performance varies considerably between physicians. Recently, proposed image-guided systems have shown promise for assisting with peripheral bronchoscopy. Yet, MDCT-based route planning to target sites has relied on tedious error-prone techniques. In addition, route planning tends not to incorporate known anatomical, device, and procedural constraints that impact a feasible route. Finally, existing systems do not effectively integrate MDCT-derived route information into the live guidance process. We propose a system that incorporates an automatic optimal route-planning method, which integrates known route constraints. Furthermore, our system offers a natural translation of the MDCT-based route plan into the live guidance strategy via MDCT/video data fusion. An image-based study demonstrates the route-planning method’s functionality. Next, we present a prospective lung-cancer patient study in which our system achieved a successful navigation rate of 91% to target sites. Furthermore, when compared to a competing commercial system, our system enabled bronchoscopy over two airways deeper into the airway-tree periphery with a sample time that was nearly 2 min shorter on average. Finally, our system’s ability to almost perfectly predict the depth of a bronchoscope’s navigable route in advance represents a substantial benefit of optimal route planning.

Description: This paper presents new structural statistical matrices which are gray level size zone matrix (SZM) texture descriptor variants. The SZM is based on the cooccurrences of size/intensity of each flat zone (connected pixels with the same gray level). The first improvement increases the information processed by merging multiple gray-level quantizations and reduces the required parameter numbers. New improved descriptors were especially designed for supervised cell texture classification. They are illustrated thanks to two different databases built from quantitative cell biology. The second alternative characterizes the DNA organization during the mitosis, according to zone intensities radial distribution. The third variant is a matrix structure generalization for the fibrous texture analysis, by changing the intensity/size pair into the length/orientation pair of each region.

Description: Galvanic coupling in intrabody communication (IBC) is a technique that couples low-power and low-frequency voltages and currents into the human body, which acts as a transmission medium, and thus constitutes a promising approach in the design of personal health devices. Despite important advances being made during recent years, the investigation of relevant galvanic IBC parameters, including the influence of human tissues and different electrode configurations, still requires further research efforts. The objective of this work is to disclose knowledge into IBC galvanic coupling transmission mechanisms by using a realistic 3-D finite element model of the human arm. Unlike other computational models for IBC, we have modeled the differential configuration of the galvanic coupling as a four-port network in order to analyze the electric field distribution and current density through different tissues. This has allowed us to provide an insight into signal transmission paths through the human body, showing them to be considerably dependent on variables such as frequency and inter-electrode distance. In addition, other important variables, for example bioimpedance and pathloss, have also been analyzed. Finally, experimental measurements were also carried out for the sake of validation, demonstrating the reliability of the model to emulate in general forms some of the behaviors observed in practice.

Description: Indicator-dilution methods are widely used by many medical imaging techniques and by dye-, lithium-, and thermodilution measurements. The measured indicator dilution curves are typically fitted by a mathematical model to estimate the hemodynamic parameters of interest. This paper presents a new maximum-likelihood algorithm for parameter estimation, where indicator dilution curves are considered as the histogram of underlying transit-time distribution. Apart from a general description of the algorithm, semianalytical solutions are provided for three well-known indicator dilution models. An adaptation of the algorithm is also introduced to cope with indicator recirculation. In simulations as well as in experimental data obtained by dynamic contrast-enhanced ultrasound imaging, the proposed algorithm shows a superior parameter estimation accuracy over nonlinear least-squares regression. The feasibility of the algorithm for use in vivo is evaluated using dynamic contrast-enhanced ultrasound recordings obtained with the purpose of prostate cancer detection. The proposed algorithm shows an improved ability (increase in receiver-operating characteristic curve area of up to 0.13) with respect to existing methods to differentiate between healthy tissue and cancer.

Description: Early detection of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is crucial for the success of the defibrillation therapy. A wide variety of detection algorithms have been proposed based on temporal, spectral, or complexity parameters extracted from the ECG. However, these algorithms are mostly constructed by considering each parameter individually. In this study, we present a novel life-threatening arrhythmias detection algorithm that combines a number of previously proposed ECG parameters by using support vector machines classifiers. A total of $hbox{13}$ parameters were computed accounting for temporal (morphological), spectral, and complexity features of the ECG signal. A filter-type feature selection (FS) procedure was proposed to analyze the relevance of the computed parameters and how they affect the detection performance. The proposed methodology was evaluated in two different binary detection scenarios: shockable (FV plus VT) versus nonshockable arrhythmias, and VF versus nonVF rhythms, using the information contained in the medical imaging technology database, the Creighton University ventricular tachycardia database, and the ventricular arrhythmia database. sensitivity (SE) and specificity (SP) analysis on the out of sample test data showed values of $hbox{SE}=hbox{95%}$ , $hbox{SP}=hbox{99%}$ , and $hbox{SE}=hbox{92%}$ , $hbox{SP}=hbox{97%}$ in the case of shockable and VF scenarios, respectively. Our algorithm was benchmarked against individual detection schemes, significantly improving their performance. Our results demonstrate that the combination of ECG parameters using statistical learning - lgorithms improves the efficiency for the detection of life-threatening arrhythmias.

Description: The imbalance of autonomic nervous system (ANS) has a close relationship to cardiac mortality. The noninvasive assessment of ANS is of great importance and remains challenging. A new method, characterizing the consistency of short-term nonlinear indexes of heart rate variability (HRV) and QT variability (QTV), is proposed and validated. Holter records from two databases in Telemetric and Holter ECG Warehouse were used, 43 records from one database (named ESRD) as typical subjects of ANS dysfunction and 118 records from the other database (named Normal) as normal controls. The consistency of HRV and QTV was characterized by estimating mutual information (MI) of paired short-term recurrence quantification analysis (RQA) indexes in resting state. The influence of physiological differences on MIs of paired RQA indexes in Normal was investigated as well. Results showed that there were significant differences in MI-DET (day: 0.283 ± 0.070 versus 0.133 ± 0.055 and night: 0.258 ± 0.061 versus 0.117 ± 0.055) and MI-LAM (day: 0.439 ± 0.053 versus 0.293 ± 0.073 and night: 0.361 ± 0.079 versus 0.241 ± 0.087) between Normal and ESRD, much reduced consistency in ESRD. For MI-DET in Normal, sex had no influence, and there was age related alternations by day but not at night. There was no influence of sex and age on MI-LAM in Normal. The sensitivity, specificity, and total accuracy for discriminating Normal and ESRD were 88.37%, 95.76%, and 93.79%, respectively. The proposed measures are shown to have the advantage in reducing the influence of physiological differences and highlighting the pathological influence, providing a promising method to find clinical application for noninvasive assessment of ANS state.