ALBERT

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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.

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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: Low-dose computed tomography (LDCT) images are often severely degraded by amplified mottle noise and streak artifacts. These artifacts are often hard to suppress without introducing tissue blurring effects. In this paper, we propose to process LDCT images using a novel image-domain algorithm called “artifact suppressed dictionary learning (ASDL).” In this ASDL method, orientation and scale information on artifacts is exploited to train artifact atoms, which are then combined with tissue feature atoms to build three discriminative dictionaries. The streak artifacts are cancelled via a discriminative sparse representation operation based on these dictionaries. Then, a general dictionary learning processing is applied to further reduce the noise and residual artifacts. Qualitative and quantitative evaluations on a large set of abdominal and mediastinum CT images are carried out and the results show that the proposed method can be efficiently applied in most current CT systems.

Description: When segmenting intraretinal layers from multiple optical coherence tomography (OCT) images forming a mosaic or a set of repeated scans, it is attractive to exploit the additional information from the overlapping areas rather than discarding it as redundant, especially in low contrast and noisy images. However, it is currently not clear how to effectively combine the multiple information sources available in the areas of overlap. In this paper, we propose a novel graph-theoretic method for multi-surface multi-field co-segmentation of intraretinal layers, assuring consistent segmentation of the fields across the overlapped areas. After 2-D en-face alignment, all the fields are segmented simultaneously, imposing a priori soft interfield-intrasurface constraints for each pair of overlapping fields. The constraints penalize deviations from the expected surface height differences, taken to be the depth-axis shifts that produce the maximum cross-correlation of pairwise-overlapped areas. The method's accuracy and reproducibility are evaluated qualitatively and quantitatively on 212 OCT images (20 nine-field, 32 single-field acquisitions) from 26 patients with glaucoma. Qualitatively, the obtained thickness maps show no stitching artifacts, compared to pronounced stitches when the fields are segmented independently. Quantitatively, two ophthalmologists manually traced four intraretinal layers on 10 patients, and the average error ( $4.58 pm 1.46 ~ {rm mu m}$ ) was comparable to the average difference between the observers ( $5.86pm 1.72 ~ {rm mu m}$ ). Furthermore, we show the benefit of the proposed approach in co-segmenting longitudinal scans. As opposed to segmenting layers in each of the fields independently, the proposed co-segmentation method obtains consistent segmentations across the overlapped areas, producing accurate, reproducibl- , and artifact-free results.

Description: Diffuse optical tomography (DOT) is a noninvasive technique which measures hemodynamic changes in the tissue with near infrared light, which has been increasingly used to study brain functions. Due to the nature of light propagation in the tissue, the reconstruction problem is severely ill-posed. For linearized DOT problems, sparsity regularization has achieved promising results over conventional Tikhonov regularization in recent experimental research. As extensions to standard sparsity, it is widely known that structured sparsity based methods are often superior in terms of reconstruction accuracy, when the data follows some structures. In this paper, we exploit the structured sparsity of diffuse optical images. Based on the functional specialization of the brain, it is observed that the in vivo absorption changes caused by a specific brain function would be clustered in certain region(s) and not randomly distributed. Thus, a new algorithm is proposed for this clustered sparsity reconstruction (CSR). Results of numerical simulations and phantom experiments have demonstrated the superiority of the proposed method over the state-of-the-art methods. An example from human in vivo measurements further confirmed the advantages of the proposed CSR method.

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Description: In ophthalmology, it is now common practice to record every surgical procedure and to archive the resulting videos for documentation purposes. In this paper, we present a solution to automatically segment and categorize surgical tasks in real-time during the surgery, using the video recording. The goal would be to communicate information to the surgeon in due time, such as recommendations to the less experienced surgeons. The proposed solution relies on the content-based video retrieval paradigm: it reuses previously archived videos to automatically analyze the current surgery, by analogy reasoning. Each video is segmented, in real-time, into an alternating sequence of idle phases, during which no clinically-relevant motions are visible, and action phases. As soon as an idle phase is detected, the previous action phase is categorized and the next action phase is predicted. A conditional random field is used for categorization and prediction. The proposed system was applied to the automatic segmentation and categorization of cataract surgery tasks. A dataset of 186 surgeries, performed by ten different surgeons, was manually annotated: ten possibly overlapping surgical tasks were delimited in each surgery. Using the content of action phases and the duration of idle phases as sources of evidence, an average recognition performance of $A_{z} = 0.832 pm 0.070$ was achieved.

Description: In this study, we propose a novel pathological lung segmentation method that takes into account neighbor prior constraints and a novel pathology recognition system. Our proposed framework has two stages; during stage one, we adapted the fuzzy connectedness (FC) image segmentation algorithm to perform initial lung parenchyma extraction. In parallel, we estimate the lung volume using rib-cage information without explicitly delineating lungs. This rudimentary, but intelligent lung volume estimation system allows comparison of volume differences between rib cage and FC based lung volume measurements. Significant volume difference indicates the presence of pathology, which invokes the second stage of the proposed framework for the refinement of segmented lung. In stage two, texture-based features are utilized to detect abnormal imaging patterns (consolidations, ground glass, interstitial thickening, tree-inbud, honeycombing, nodules, and micro-nodules) that might have been missed during the first stage of the algorithm. This refinement stage is further completed by a novel neighboring anatomy-guided segmentation approach to include abnormalities with weak textures, and pleura regions. We evaluated the accuracy and efficiency of the proposed method on more than 400 CT scans with the presence of a wide spectrum of abnormalities. To our best of knowledge, this is the first study to evaluate all abnormal imaging patterns in a single segmentation framework. The quantitative results show that our pathological lung segmentation method improves on current standards because of its high sensitivity and specificity and may have considerable potential to enhance the performance of routine clinical tasks.

Description: In pinhole single photon emission computed tomography (SPECT), multi-pinhole collimators can increase sensitivity but may lead to projection overlap, or multiplexing, which can cause image artifacts. In this work, we explore whether a stacked-detector configuration with a germanium and a silicon detector, used with $^{123}{rm I}$ (27–32, 159 keV), where little multiplexing occurs in the Si projections, can reduce image artifacts caused by highly-multiplexed Ge projections. Simulations are first used to determine a reconstruction method that combines the Si and Ge projections to maximize image quality. Next, simulations of different pinhole configurations (varying projection multiplexing) in conjunction with digital phantoms are used to examine whether additional Si projections mitigate artifacts from the multiplexing in the Ge projections. Reconstructed images using both Si and Ge data are compared to those using Ge data alone. Normalized mean-square error and normalized standard deviation provide a quantitative evaluation of reconstructed images' error and noise, respectively, and are used to evaluate the impact of the additional nonmultiplexed data on image quality. For a qualitative comparison, the differential point response function is used to examine multiplexing artifacts. Results show that in cases of highly-multiplexed Ge projections, the addition of low-multiplexed Si projections helps to reduce image artifacts both quantitatively and qualitatively.

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: An easily implementable tissue cancellation method for dual energy mammography is proposed to reduce anatomical noise and enhance lesion visibility. For dual energy calibration, the images of an imaging object are directly mapped onto the images of a customized calibration phantom. Each pixel pair of the low and high energy images of the imaging object was compared to pixel pairs of the low and high energy images of the calibration phantom. The correspondence was measured by absolute difference between the pixel values of imaged object and those of the calibration phantom. Then the closest pixel pair of the calibration phantom images is marked and selected. After the calibration using direct mapping, the regions with lesion yielded different thickness from the background tissues. Taking advantage of the different thickness, the visibility of cancerous lesions was enhanced with increased contrast-to-noise ratio, depending on the size of lesion and breast thickness. However, some tissues near the edge of imaged object still remained after tissue cancellation. These remaining residuals seem to occur due to the heel effect, scattering, nonparallel X-ray beam geometry and Poisson distribution of photons. To improve its performance further, scattering and the heel effect should be compensated.

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

Description: Diffusion tensor imaging is widely used in brain connectivity research. As more and more studies recruit large numbers of subjects, it is important to design registration methods which are not only theoretically rigorous, but also computationally efficient. However, the requirement of reorienting diffusion tensors complicates and considerably slows down registration procedures, due to the correlated impacts of registration forces at adjacent voxel locations. Based on the diffeomorphic Demons algorithm (Vercauteren , 2009), we propose a fast local trust region algorithm for handling inseparable registration forces for quadratic energy functions. The method guarantees that, at any time and at any voxel location, the velocity is always within its local trust region. This local regularization allows efficient calculation of the transformation update with numeric integration instead of completely solving a large linear system at every iteration. It is able to incorporate exact reorientation and regularization into the velocity optimization, and preserve the linear complexity of the diffeomorphic Demons algorithm. In an experiment with 84 diffusion tensor images involving both pair-wise and group-wise registrations, the proposed algorithm achieves better registration in comparison with other methods solving large linear systems (Yeo , 2009). At the same time, this algorithm reduces the computation time and memory demand tenfold.

Description: Routine ultrasound exam in the second and third trimesters of pregnancy involves manually measuring fetal head and brain structures in 2-D scans. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. This paper proposes an automatic fetal head and brain (AFHB) system for automatically measuring anatomical structures from 3-D ultrasound volumes. The system searches the 3-D volume in a hierarchy of resolutions and by focusing on regions that are likely to be the measured anatomy. The output is a standardized visualization of the plane with correct orientation and centering as well as the biometric measurement of the anatomy. The system is based on a novel framework for detecting multiple structures in 3-D volumes. Since a joint model is difficult to obtain in most practical situations, the structures are detected in a sequence, one-by-one. The detection relies on Sequential Estimation techniques, frequently applied to visual tracking. The interdependence of structure poses and strong prior information embedded in our domain yields faster and more accurate results than detecting the objects individually. The posterior distribution of the structure pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple structures and hierarchical levels. The probabilistic model helps solve many challenges present in the ultrasound images of the fetus such as speckle noise, signal drop-out, shadows caused by bones, and appearance variations caused by the differences in the fetus gestational age. This is possible by discriminative learning on an extensive database of scans comprising more than two thousand volumes and more than thirteen thousand annotations. The average difference between ground truth and automatic - easurements is below 2 mm with a running time of 6.9 s (GPU) or 14.7 s (CPU). The accuracy of the AFHB system is within inter-user variability and the running time is fast, which meets the requirements for clinical use.

Description: In clinical practice, echocardiographers are often unkeen to make the significant time investment to make additional multiple measurements of Doppler velocity. Main hurdle to obtaining multiple measurements is the time required to manually trace a series of Doppler traces. To make it easier to analyze more beats, we present the description of an application system for automated aortic Doppler envelope quantification, compatible with a range of hardware platforms. It analyses long Doppler strips, spanning many heartbeats, and does not require electrocardiogram to separate individual beats. We tested its measurement of velocity-time-integral and peak-velocity against the reference standard defined as the average of three experts who each made three separate measurements. The automated measurements of velocity-time-integral showed strong correspondence $({rm R}^{2} =0.94)$ and good Bland–Altman agreement $({rm SD} = 1.39~{hbox {cm}})$ with the reference consensus expert values, and indeed performed as well as the individual experts ( ${rm R}^{2} = 0.90$ to 0.96, ${rm SD} = 1.05$ to 1.53 cm). The same performance was observed for peak-velocities; ( ${rm R}^{2} =0.98$ , ${rm SD} = 3.07~{rm cm/s}$ ) and ( ${rm R}^{2} = 0.93$ to 0.98, ${rm SD} = 2.96$ to 5.18 cm/s). This automated technology allows $〉 10$ times as many beats to be analyzed compared to the conventional manua- approach. This would make clinical and research protocols more precise for the same operator effort.

Description: We study the problem of joint registration and deformation analysis of endometrial tissue using 3D magnetic resonance imaging (MRI) and 2D trans-vaginal ultrasound (TVUS) measurements. In addition to the different imaging techniques involved in the two modalities, this problem is complicated due to: 1) different patient pose during MRI and TVUS observations, 2) the 3D nature of MRI and 2D nature of TVUS measurements, 3) the unknown intersecting plane for TVUS in MRI volume, and 4) the potential deformation of endometrial tissue during TVUS measurement process. Focusing on the shape of the tissue, we use expert manual segmentation of its boundaries in the two modalities and apply, with modification, recent developments in shape analysis of parametric surfaces to this problem. First, we extend the 2D TVUS curves to generalized cylindrical surfaces through replication, and then we compare them with MRI surfaces using elastic shape analysis. This shape analysis provides a simultaneous registration (optimal reparameterization) and deformation (geodesic) between any two parametrized surfaces. Specifically, it provides optimal curves on MRI surfaces that match with the original TVUS curves. This framework results in an accurate quantification and localization of the deformable endometrial cells for radiologists, and growth characterization for gynecologists and obstetricians. We present experimental results using semi-synthetic data and real data from patients to illustrate these ideas.

Description: Repetitive and alternating lower limb movements are a specific component of human gait. Due to technical challenges, the neural mechanisms underlying such movements have not been previously studied with functional magnetic resonance imaging. In this study, we present a novel treadmill device employed to investigate the kinematics and the brain activation patterns involved in alternating and repetitive movements of the lower limbs. Once inside the scanner, 19 healthy subjects were guided by two visual cues and instructed to perform a motor task which involved repetitive and alternating movements of both lower limbs while selecting their individual comfortable amplitude on the treadmill. The device facilitated the performance of coordinated stepping while registering the concurrent lower-limb displacements, which allowed us to quantify some movement primary kinematic features such as amplitude and frequency. During stepping, significant blood oxygen level dependent signal increases were observed bilaterally in primary and secondary sensorimotor cortex, the supplementary motor area, premotor cortex, prefrontal cortex, superior and inferior parietal lobules, putamen and cerebellum, regions that are known to be involved in lower limb motor control. Brain activations related to individual adjustments during motor performance were identified in a right lateralized network including striatal, extrastriatal, and fronto-parietal areas.

Description: 2D/3D registration of patient vasculature from preinterventional computed tomography angiography (CTA) to interventional X-ray angiography is of interest to improve guidance in percutaneous coronary interventions. In this paper we present a novel feature based 2D/3D registration framework, that is based on probabilistic point correspondences, and show its usefulness on aligning 3D coronary artery centerlines derived from CTA images with their 2D projection derived from interventional X-ray angiography. The registration framework is an extension of the Gaussian mixture model (GMM) based point-set registration to the 2D/3D setting, with a modified distance metric. We also propose a way to incorporate orientation in the registration, and show its added value for artery registration on patient datasets as well as in simulation experiments. The oriented GMM registration achieved a median accuracy of 1.06 mm, with a convergence rate of 81% for nonrigid vessel centerline registration on 12 patient datasets, using a statistical shape model. The method thereby outperformed the iterative closest point algorithm, the GMM registration without orientation, and two recently published methods on 2D/3D coronary artery registration.

Description: In a positron emission tomography (PET) study, the local uptake of the tracer is dependent on vascular delivery and retention. For dynamic studies the measured uptake time-course information can be best interpreted when knowledge of the time-course of tracer in the blood is available. This is certainly true for the most established tracers such as $^{18}F$ -Fluorodeoxyglucose (FDG) and $^{15}O$ -Water (H $_2$ O). Since direct sampling of blood as part of PET studies is increasingly impractical, there is ongoing interest in image-extraction of blood time-course information. But analysis of PET-measured blood pool signals is complicated because they will typically involve a combination of arterial, venous and tissue information. Thus, a careful appreciation of these components is needed to interpret the available data. To facilitate this process, we propose a novel Markov chain model for representation of the circulation of a tracer atom in the body. The model represents both arterial and venous time-course patterns. Under reasonable conditions equilibration of tracer activity in arterial and venous blood is achieved by the end of the PET study—consistent with empirical measurement. Statistical inference for Markov model parameters is a challenge. A penalized nonlinear least squares process, incorporating a generalized cross-validation score, is proposed. Random effects analysis is used to adaptively specify the structure of the penalty function based on historical samples of directly measured blood data. A collection of arterially sampled data from PET studies with FDG and H $_2$ O is used to illustrate the methodology. These data analyses are highly supportive of the overall modeling approach. An adapt- tion of the model to the problem of extraction of arterial blood signals from imaging data is also developed and promising preliminary results for cerebral and thoracic imaging studies with FDG and H $_2$ O are obtained.

Description: Digital image-based elasto-tomography (DIET) is a prototype system for breast cancer screening. A breast is imaged while being vibrated, and the observed surface motion is used to infer the internal stiffness of the breast, hence identifying tumors. This paper describes a computer vision system for accurately measuring 3-D surface motion. A model-based segmentation is used to identify the profile of the breast in each image, and the 3-D surface is reconstructed by fitting a model to the profiles. The surface motion is measured using a modern optical flow implementation customized to the application, then trajectories of points on the 3-D surface are given by fusing the optical flow with the reconstructed surfaces. On data from human trials, the system is shown to exceed the performance of an earlier marker-based system at tracking skin surface motion. We demonstrate that the system can detect a 10 mm tumor in a silicone phantom breast.

Description: Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time.

Description: Prostate cancer is one of the major causes of cancer death for men in the western world. Magnetic resonance imaging (MRI) is being increasingly used as a modality to detect prostate cancer. Therefore, computer-aided detection of prostate cancer in MRI images has become an active area of research. In this paper we investigate a fully automated computer-aided detection system which consists of two stages. In the first stage, we detect initial candidates using multi-atlas-based prostate segmentation, voxel feature extraction, classification and local maxima detection. The second stage segments the candidate regions and using classification we obtain cancer likelihoods for each candidate. Features represent pharmacokinetic behavior, symmetry and appearance, among others. The system is evaluated on a large consecutive cohort of 347 patients with MR-guided biopsy as the reference standard. This set contained 165 patients with cancer and 182 patients without prostate cancer. Performance evaluation is based on lesion-based free-response receiver operating characteristic curve and patient-based receiver operating characteristic analysis. The system is also compared to the prospective clinical performance of radiologists. Results show a sensitivity of 0.42, 0.75, and 0.89 at 0.1, 1, and 10 false positives per normal case. In clinical workflow the system could potentially be used to improve the sensitivity of the radiologist. At the high specificity reading setting, which is typical in screening situations, the system does not perform significantly different from the radiologist and could be used as an independent second reader instead of a second radiologist. Furthermore, the system has potential in a first-reader setting.

Description: Quantification of regional myocardial motion and deformation from cardiac ultrasound is fostering considerable research efforts. Despite the tremendous improvements done in the field, all existing approaches still face a common limitation which is intrinsically connected with the formation of the ultrasound images. Specifically, the reduced lateral resolution and the absence of phase information in the lateral direction highly limit the accuracy in the computation of lateral displacements. In this context, this paper introduces a novel setup for the estimation of cardiac motion with ultrasound. The framework includes an unconventional beamforming technique and a dedicated motion estimation algorithm. The beamformer aims at introducing phase information in the lateral direction by producing transverse oscillations. The estimator directly exploits the phase information in the two directions by decomposing the image into two 2-D single-orthant analytic signals. An in silico evaluation of the proposed framework is presented on five ultra-realistic simulated echocardiographic sequences, where the proposed motion estimator is contrasted against other two phase-based solutions exploiting the presence of transverse oscillations and against block-matching on standard images. An implementation of the new beamforming strategy on a research ultrasound platform is also shown along with a preliminary in vivo evaluation on one healthy subject.

Description: In this paper different model-based methods of classification of global patterns in dermoscopic images are proposed. Global patterns identification is included in the pattern analysis framework, the melanoma diagnosis method most used among dermatologists. The modeling is performed in two senses: first a dermoscopic image is modeled by a finite symmetric conditional Markov model applied to $L^{ast}a^{ast}b^{ast}$ color space and the estimated parameters of this model are treated as features. In turn, the distribution of these features are supposed that follow different models along a lesion: a Gaussian model, a Gaussian mixture model, and a bag-of-features histogram model. For each case, the classification is carried out by an image retrieval approach with different distance metrics. The main objective is to classify a whole pigmented lesion into three possible patterns: globular, homogeneous, and reticular. An extensive evaluation of the performance of each method has been carried out on an image database extracted from a public Atlas of Dermoscopy. The best classification success rate is achieved by the Gaussian mixture model-based method with a 78.44% success rate in average. In a further evaluation the multicomponent pattern is analyzed obtaining a 72.91% success rate.

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Description: Multispectral optoacoustic tomography (MSOT) utilizes broadband ultrasound detection for imaging biologically-relevant optical absorption features at a range of scales. Due to the multiscale and multispectral features of the technology, MSOT comes with distinct requirements in implementation and data analysis. In this work, we investigate the interplay between scale, which depends on ultrasonic detection frequency, and optical multispectral spectral analysis, two dimensions that are unique to MSOT and represent a previously unexplored challenge. We show that ultrasound frequency-dependent artifacts suppress multispectral features and complicate spectral analysis. In response, we employ a wavelet decomposition to perform spectral unmixing on a per-scale basis (or per ultrasound frequency band) and showcase imaging of fine-scale features otherwise hidden by low frequency components. We explain the proposed algorithm by means of simple simulations and demonstrate improved performance in imaging data of blood vessels in human subjects.

Description: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.

Description: The purpose of this study was to increase the sensitivity of XFCT imaging by optimizing the data acquisition geometry for reduced scatter X-rays. The placement of detectors and detector energy window were chosen to minimize scatter X-rays. We performed both theoretical calculations and Monte Carlo simulations of this optimized detector configuration on a mouse-sized phantom containing various gold concentrations. The sensitivity limits were determined for three different X-ray spectra: a monoenergetic source, a Gaussian source, and a conventional X-ray tube source. Scatter X-rays were minimized using a backscatter detector orientation (scatter direction $〉 110^{circ }$ to the primary X-ray beam). The optimized configuration simultaneously reduced the number of detectors and improved the image signal-to-noise ratio. The sensitivity of the optimized configuration was 10 $mu {rm g}/{rm mL}$ (10 pM) at 2 mGy dose with the mono-energetic source, which is an order of magnitude improvement over the unoptimized configuration (102 pM without the optimization). Similar improvements were seen with the Gaussian spectrum source and conventional X-ray tube source. The optimization improvements were predicted in the theoretical model and also demonstrated in simulations. The sensitivity of XFCT imaging can be enhanced by an order of magnitude with the data acquisition optimization, greatly enhancing the potential of this modality for future use in clinical molecular imaging.

Description: Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an emerging computed biomedical imaging modality that exploits optical contrast and ultrasonic detection principles. Iterative image reconstruction algorithms that are based on discrete imaging models are actively being developed for OAT due to their ability to improve image quality by incorporating accurate models of the imaging physics, instrument response, and measurement noise. In this work, we investigate the use of discrete imaging models based on Kaiser–Bessel window functions for iterative image reconstruction in OAT. A closed-form expression for the pressure produced by a Kaiser–Bessel function is calculated, which facilitates accurate computation of the system matrix. Computer-simulation and experimental studies are employed to demonstrate the potential advantages of Kaiser–Bessel function-based iterative image reconstruction in OAT.

Description: The multi-channel nature of digital histopathological images presents an opportunity to exploit the correlated color channel information for better image modeling. Inspired by recent work in sparsity for single channel image classification, we propose a new simultaneous sparsity model for multi-channel histopathological image representation and classification (SHIRC). Essentially, we represent a histopathological image as a sparse linear combination of training examples under suitable channel-wise constraints. Classification is performed by solving a newly formulated simultaneous sparsity-based optimization problem. A practical challenge is the correspondence of image objects (cellular and nuclear structures) at different spatial locations in the image. We propose a robust locally adaptive variant of SHIRC (LA-SHIRC) to tackle this issue. Experiments on two challenging real-world image data sets: 1) mammalian tissue images acquired by pathologists of the animal diagnostics lab (ADL) at Pennsylvania State University, and 2) human intraductal breast lesions, reveal the merits of our proposal over state-of-the-art alternatives. Further, we demonstrate that LA-SHIRC exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training per class is often not available.

Description: Advertisement: This publication offers open access options for authors. IEEE open access publishing.

Description: This study presents an investigation of the relationship between internalization of ISO 9000 practices and the development of intellectual capital within organizations. We posit that intellectual capital, which comprises of human capital, organizational capital, and social capital, leads to improved processes in an organization. Process improvement, in turn, results in superior operational performance. Using a sample of 321 ISO 9001 certified organizations, the hypothesized relationships are tested by means of the structural equation modeling technique. The results of this study lend strong support for the hypotheses and show that performance benefits from the internalization of ISO 9000 standards occur mainly through the development of human and organizational capital, and the consequent improvement in business processes. This study discusses theoretical and managerial implications, and presents directions for future research.

Description: This paper presents a methodology for developing a spare parts inventory management system with a focus on the nuclear power sector. Often, demand for spare parts is highly intermittent and cannot be accurately forecasted through traditional methods. Examples include nuclear power generation equipment, ground space systems, and aircraft engine parts. We take a data-driven engineering management approach and develop a four-step methodology for spare parts management in such environments. These steps comprise an influence diagram for identifying relevant factors, weighting of influences through the analytic hierarchy process, grouping parts according to inventory criticality indices, and the development of base stock inventory policies for each group. This approach allows the system to be actively managed within a continuous improvement framework through employee engagement and input, and mathematical assumptions are not made in the models. To our knowledge, no such integrated, comprehensive methodology for spare parts has been developed. The techniques employed in this research can be effectively used together to holistically manage the entire spare parts process, or they may be used separately to manage portions of the process. This paper provides an overview of the methodology, and the entire approach is illustrated via a test bed nuclear power generation facility.

Description: This study investigates the influence of supply chain portfolios on the paradox of exploration and exploitation within interfirm collaborations. This study deploys two supply chain portfolio characteristics, supplier diversity and repeated partnerships, to explain the evolution of technological innovations and supply chain portfolios. It tests a set of hypotheses using a longitudinal study of 76 Taiwanese high-tech companies between 2001 and 2005. The findings support the premise that a diverse supplier base and deep familiarity with individual suppliers stimulate firms’ exploration and exploitation activities. This study also finds that the threshold effects of these two characteristics on exploration activities clarify how determinants of firms’ supply chain portfolios influence the developmental paths in the firm's evolution of exploration and exploitation activities.

Description: Researchers have argued that specialization within groups yields productivity gains. We evaluate this statement with a focus on groups of Ph.D. students. Using an established technique in computer science called Latent Dirichlet Allocation, we construct a novel measure of the dispersion of Ph.D. students’ research interests based on their dissertation abstracts. We then relate this measure to Ph.D. group publications. For our study, we use a rich dataset on groups of Ph.D. students who studied at a major Swiss University, during the 1993–2008 period. We find robust evidence that within-group knowledge specialization is associated with a larger number of publications. However, when specialization increases beyond a critical level, it hinders the group’s publication output. We interpret these results as an indication that gains, in the amount of research output, can be achieved if Ph.D. students specialize according to their comparative advantages. However, beyond a certain level, knowledge specialization has a detrimental impact on research output, due to increasing communication costs and an increased likelihood of conflict insurgence.

Description: This study elucidates how a firm accesses external technological knowledge (TK) and utilizes such TK, facilitated by slack resources, to determine the scope of technological development. We examine data from 947 cooperative technology agreements among 248 electronics and information technology firms in Taiwan. The results demonstrate that there is an inverted U-shaped relationship between TK distance and the scope of technological development. Furthermore, we find that the moderating effect of unabsorbed slack can extend the inverted U-shaped relationship in the direction of higher levels of technological diversification and greater TK distance. Additionally, unabsorbed and absorbed slack, respectively, moderate the linear relationship between TK distance and a firm's scope of technological development toward two striking favorable contrasts: technological diversification and specialization. Unabsorbed slack plays a positive catalytic role in the creation of technological diversification; however, absorbed slack does not serve such a role.

Description: This paper develops a scenario generation framework for modeling demand and supply uncertainties. This framework is applied to a decision problem at an illustrative manufacturing company facing risks caused by uncertainties in end product demand and component availability. In particular, we focus on the impact of interdependent demand and supply uncertainty. To evaluate analytically the impact of interdependence between these two uncertainties, we analyze a simple newsvendor model with a bivariate normal distribution for demand and supply yield. Then, in more complex settings, we use scenarios based on copula functions, which can account for both linear and nonlinear dependences. These scenarios are used in conjunction with stochastic programming models to evaluate sourcing costs and risks under various conditions. In particular, we study a decision problem faced by a manufacturer which uses capacity reservation contracts in the presence of an unreliable supplier and uncertain product demand. Our results indicate that worst-case risks grow significantly when the dependences in uncertainties about demand and supply yield are stronger. We also highlight differences caused by linear and tail-dependent dependence structures. Based on the results, we argue that using the proposed framework to model uncertainties and their interdependences allows managers to better manage risks.

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

Description: Design for supply chain has become an essential consideration while designing a new product. Previous studies pointed out that early supplier involvement can contribute to the success of the product development and enhance the competitive advantage of the enterprise. However, most of the studies coordinating supplier selection and supply chain configuration make these decisions during the detail design stage, which is the last phase of product design. This research aims to investigate the supply chain scenarios for their relevant performance at the conceptual design stage. The performance of different supply chain scenarios (i.e., centralized and decentralized), are compared and discussed. The results show that the decentralized supply chain scenario is advantageous for the time performance of the supply chain network, whereas the centralized supply chain scenario demonstrates superiority on the cost performance.

Description: In a previous paper in this journal named The TOC-Based Algorithm for Solving Multiple Constraint Resources, Ray considered an integrated heuristic approach named AHP/TOC comprising the analytic hierarchy process (AHP) and theory of constraints (TOC) in a multiple constraints resource product mix problem. This paper gives three typical examples and points out that the proposed approach would not generate the optimal solution. Furthermore, the reasons are analyzed in terms of the ranking approach of product priority using AHP and the adjustment approach of product mix after a new bottleneck has been identified. We clarify the cases under which the AHP/TOC method can and cannot output the optimal solution. Finally, some possible improvements are given.

Description: Rapid changes of new technologies, market dynamics, and swift fluctuation of customer tastes acerbate the needs for companies to identifying the emerging customer requirements and incorporate them in the conceptual design stage. Discovering emerging customer needs has great potential to create new product opportunities for the success of business. However, identifying customers’ requirements in an early design stage has not been well addressed in the traditional design methodology. Because emerging needs are usually not obvious at the budding stage, the related observations are rare in dataset. Traditional methods fall short of providing enough support of eliciting the below-the-radar needs. This paper reports a new approach to identify emerging customer needs in the growing popular online interactive environment with customer participation. The Bayes factor, a methodology to quantify the occurrence possibility of a certain event, is used to calculate the likelihood that current offering cannot meet the customer's requirements whenever a new specification is incorporated. With the sequential input from customers, a series of the Bayes factor value can then be calculated as the weight of evidence that emerging customer needs appear. We show that the decreased value will be the potentially emerging needs which cannot be satisfied by the current product family. Numerical and analytical results are derived to demonstrate the viability and effectiveness of the proposed approach.

Description: Interorganizational relationships are an integral part of an organization's decision-making process. The reason is that an organization's decision affects and is affected by the decision of its partners, particularly in the context of technology adoption and diffusion. Therefore, it is important to explore the role of this social network of organizations in shaping the process of technology diffusion. We conduct a series of experiments using the agent-based simulation modeling technique to identify strategically located firms in a network that can significantly influence the process of software diffusion. Our findings suggest that: 1) structural location of organizations does significantly influence the process of diffusion; and 2) the criteria for identifying strategically located firms depend on contextual factors, such as network topology, network density, and interoperability costs. To illustrate the practical use of these results, we propose a “network-aware” pricing strategy that takes advantage of information regarding organizational social networks. We illustrate that contrary to prevalent practice in the enterprise software market, where firms are generally targeted on the basis of their size, network-aware pricing has the potential to facilitate the development of a more effective market penetration strategy.

Description: This study extends quality management from an individual company perspective to a supply chain perspective. We propose a concept of supply chain quality integration (SCQI) that consists of internal, supplier, and customer integration for quality improvement, and develop a model that specifies the relationships among competitive hostility, the organization-wide approach to quality, three types of SCQI, and quality-related performance. We test the model using data collected from 291 high-performance manufacturing plants from ten countries. The results indicate that competitive hostility has a positive effect on the organization-wide approach to quality, and that both have positive effects on SCQI. In addition, internal quality integration significantly enhances external quality integration with both suppliers and customers. Further, internal quality integration significantly improves all quality-related performance (i.e., product quality, cost, delivery, and flexibility), and both supplier and customer quality integration significantly improve cost performance. Whereas customer quality integration significantly improves delivery performance and supplier quality integration significantly improves quality performance, only internal quality integration can improve flexibility performance. The findings reveal how different types of SCQI are related to quality-related performance and highlight internal quality integration as a core strategic resource for quality improvement. As such, they provide important managerial insights for supply chain quality managers to improve quality-related performance.

Description: This paper considers a sourcing problem faced by a manufacturer who outsources the manufacturing of a product to one of several competing suppliers, whose cost and quality capabilities are unknown. We consider a two-stage sourcing process in which the first stage is the qualification stage, while the second stage is the supplier selection stage. In the first stage, the manufacturer exerts effort to learn about the quality level of each of the suppliers and then must determine the set of qualified suppliers, subject to some tolerance for error. In the second stage, the manufacturer runs a price-only procurement auction, in which the qualified suppliers compete for the manufacturer’s business. We model this two-stage sourcing process with the goal of obtaining insights into manufacturer’s optimal decisions. We seek to determine the optimal qualification standard, the optimal amount of effort to be exerted in the qualification process and the appropriate tolerance for error in the qualification process, and to understand the interactions between these decision variables. We are particularly interested in understanding how the manufacturer can design the process to 1) ensure the firm only sources from qualified suppliers and 2) encourage competition among the suppliers during supplier selection.

Description: We explore the boundary conditions of Christensen's theory of disruption. Using technological changes that were potentially disruptive to the manufacturers of industrial robots as the context, we find that, unlike Christensen's suggestions, large hydraulic robot manufacturers introduced new products with the disruptive technology fairly early during the changes. We extend Christensen's theory by suggesting that the boundary conditions of the theory are more nuanced than what the extant literature suggests.

Description: To gain competitive leverage, firms that design and develop complex products seek to optimize the organization structure in their new product development (PD) projects. The overlapped process is a fundamental feature of concurrent engineering, which not only reduces project duration but also increases coordination, communication, and interaction between organizational units. In order to reduce complexity of coordination, this paper proposes a design structure matrix (DSM) method for measuring interaction strength and clustering organizational units. This paper analyzes the impact of overlapping on interaction strength between teams performing these overlapped activities. Further, evolution DSM and sensitivity DSM are constructed for representing evolution degree and sensitivity degree. Based on the concepts of overlapping ratio and relative coordination frequency, this paper proposes a quantitative model using the evolution DSM and sensitivity DSM to measure the interaction strength between teams performing overlapped activities. The two-stage clustering criterion model is proposed for clustering numerical DSM, of which the first-stage clustering criterion is the maximization of the added average interaction strength of the selected organizational units and the second-stage clustering criterion is the minimization of the total coordination time of the PD project. An industrial example is provided to illustrate the proposed model. Results indicate that the clustered numerical DSM can reduce coordination time significantly. The model yields and reinforces several managerial insights, including: how to analyze the interaction strength based on overlapping, the impact of interaction strength on clusters and coordination time.

Description: In the last decade, industrial research for sustainable development and environment has become more strategically oriented, due to increasing of environmental pressures coming from local laws, stakeholders, and final customers. The goal of this research is to create a methodical approach for integrating the development of environmental and quality audits by prioritizing corrective actions (CA). The work starts and develops within a firm that produces smartcards for mobile phones, where environmental audits have been integrated with quality audits by a fuzzy inference engine that addresses gravity determinations of environmental and quality nonconformities. The findings of this study provided some general principles to integrate environmental management system (EMS) in quality management system (QMS) development and suggested an implementation of Continuous Improvement, which addresses, analyzes, prioritizes, and measures activities within the Deming wheel.

Description: An ongoing challenge that enterprises currently face is that of fostering agile mindsets and behaviors among employees as necessary to achieve enterprise agility by leveraging IT. Although existing studies focus on the shortening business processes and adopting agile methods of system implementation, little attention is directed toward the “people” factor as a critical factor in enterprise agility. As a means to inspire agile mindsets and behaviors in employees, our study proposes an important concept, organizational identity of IT department which encourages organization members to achieve IT-enabled enterprise agility. We term such an identity as IT organizational identity. As rapid environmental changes continue, a firm's IT department should develop different types of IT organizational identities to maintain agile behaviors and mindsets among its employees. Therefore, our study reveals how IT departments can develop appropriate and positive organizational identity at various stages of organizational development.