ALBERT

Beschreibung: In recent years, in an attempt to maximize performance, machine learning approaches for event-related potential (ERP) spelling have become more and more complex. In this paper, we have taken a step back as we wanted to improve the performance without building an overly complex model, that cannot be used by the community. Our research resulted in a unified probabilistic model for ERP spelling, which is based on only three assumptions and incorporates language information. On top of that, the probabilistic nature of our classifier yields a natural dynamic stopping strategy. Furthermore, our method uses the same parameters across 25 subjects from three different datasets. We show that our classifier, when enhanced with language models and dynamic stopping, improves the spelling speed and accuracy drastically. Additionally, we would like to point out that as our model is entirely probabilistic, it can easily be used as the foundation for complex systems in future work. All our experiments are executed on publicly available datasets to allow for future comparison with similar techniques.

Beschreibung: Compressive sensing has shown significant promise in biomedical fields. It reconstructs a signal from sub-Nyquist random linear measurements. Classical methods only exploit the sparsity in one domain. A lot of biomedical signals have additional structures, such as multi-sparsity in different domains, piecewise smoothness, low rank, etc. We propose a framework to exploit all the available structure information. A new convex programming problem is generated with multiple convex structure-inducing constraints and the linear measurement fitting constraint. With additional a priori information for solving the underdetermined system, the signal recovery performance can be improved. In numerical experiments, we compare the proposed method with classical methods. Both simulated data and real-life biomedical data are used. Results show that the newly proposed method achieves better reconstruction accuracy performance in term of both L1 and L2 errors.

Beschreibung: Transcranial magnetic stimulation (TMS) is a tool for noninvasive stimulation of neuronal tissue used for research in cognitive neuroscience and to treat neurological disorders. Many TMS applications call for large electric fields to be sharply focused on regions that often lie deep inside the brain. Unfortunately, the fields generated by present-day TMS coils diffuse and decay rapidly as they penetrate into the head. As a result, they tend to stimulate relatively large regions of tissue near the brain surface. Earlier studies suggested that a focused TMS excitation can be attained using multiple nonuniformly fed coils in a multichannel array. We propose a systematic, genetic algorithm-based technique for synthesizing multichannel arrays that minimize the volume of the excited region required to achieve a prescribed penetration depth and maintain realistic values for the input driving currents. Because multichannel arrays are costly to build, we also propose a method to convert the multichannel arrays into single-channel ones while minimally materially deteriorating performance. Numerical results show that the new multi- and single-channel arrays stimulate tissue 2.4 cm into the head while exciting 3.0 and 2.6 times less volume than conventional Figure-8 coils, respectively.

Beschreibung: In difficult epileptic patients, the brain structures are explored by means of depth multicontact electrodes [stereoelectroencephalography (SEEG)]. Recently, a novel diagnostic technique allows an accurate definition of the epileptogenic zone using deep brain stimulation (DBS). The stimulation signal propagates in the brain and thus it appears on most of the other SEEG electrodes, masking the local brain electrophysiological activity. The objective of this paper is the DBS-SEEG signals detrending and denoising in order to recover the masked physiological sources. We review the main filtering methods and put forward an approach based on the combination of filtering with generalized eigenvalue decomposition (GEVD). An experimental study on simulated and real SEEG shows that our approach is able to separate DBS sources from brain activity. The best results are obtained by an original singular spectrum analysis-GEVD approach.

Beschreibung: This paper presents a novel palpation probe based on optical fiber technology. It is designed to measure stiffness distribution of a soft tissue while sliding over the tissue surface in a near frictionless manner. A novelty of the probe is its ability to measure indentation depth for nonplanar tissue profiles which are commonly experienced during surgery. Since tumors are often harder than the surrounding tissue, the proposed probe can intraoperatively aid the surgeon to rapidly identify the presence, location, and size of the tumors through the generation of a tissue stiffness map. The probe can concurrently measure tissue reaction force, indentation depth, and the orientation of the probe with respect to the tissue surface. Hence, it can generate an elasticity model of the tissue with minimum measurement inaccuracies caused by surface profile variations. Further, the probe has a tunable force range and the indentation force can be adjusted externally to match tissue limitations. The performance of the probe developed was validated using simulated soft tissues samples. Our tumor identification experiments showed that the probe can accurately identify the location and size of tumors hidden inside nonflat tissue surfaces. Further, the probe has clearly demonstrated its potential to identify tumors with tumor–tissue stiffness ratios as low as 2.1.

Beschreibung: We introduce a novel method for the automatic detection of atrial fibrillation (AF) using time-varying coherence functions (TVCF). The TVCF is estimated by the multiplication of two time-varying transfer functions (TVTFs). The two TVTFs are obtained using two adjacent data segments with one data segment as the input signal and the other data segment as the output to produce the first TVTF; the second TVTF is produced by reversing the input and output signals. We found that the resultant TVCF between two adjacent normal sinus rhythm (NSR) segments shows high coherence values (near 1) throughout the entire frequency range. However, if either or both segments partially or fully contain AF, the resultant TVCF is significantly lower than 1. When TVCF was combined with Shannon entropy (SE), we obtained even more accurate AF detection rate of 97.9% for the MIT-BIH atrial fibrillation (AF) database $(n$ = 23) with 128 beat segments. The detection algorithm was tested on four databases using 128 beat segments: the MIT-BIH AF database, the MIT-BIH NSR database ( $n$ = 18), the MIT-BIH Arrhythmia database ( $n$ = 48), and a clinical 24-h Holter AF database ( $n$ = 25). Using the receiver operating characteristic curves from the combination of TVCF and SE, we obtained a sensitivity of 98.2% and specificity of 97.7% for the MIT-BIH AF database. For the MIT-BIH NSR database, we found a specificity of 99.7%. For the MIT-BIH Arrhythmia database, the sensitivity and specificity were 91.1% and 89.7%, respectively. For the clinical database (24-h Holter data), the sensitivity and specificity were 92.3% and 93.6%, respectively. We - lso found that a short segment (12 beats) also provided accurate AF detection for all databases: sensitivity of 94.7% and specificity of 90.4% for the MIT-BIH AF, specificity of 94.4% for the MIT-BIH-NSR, the sensitivity of 92.4% and specificity of 84.1% for the MIT-BIH arrhythmia, and sensitivity of 93.9% and specificity of 84.4% for the clinical database. The advantage of using a short segment is more accurate AF burden calculation as the timing of transitions between NSR and AF are more accurately detected.

Beschreibung: In functional MRI, it is often desirable to reduce the readout duration to make the acquired data less prone to $T_2^*$ susceptibility artifacts. In addition, a shorter readout length allows for a shorter minimum TE, which is important for optimizing SNR. This can be achieved by undersampling the $k$ -space. However, the conventional Fourier transform-based reconstruction method suffers from under-sampling artifacts such as high-frequency ringing and loss of resolution. To address this problem, we revisit the constrained-model approach using the generalized-series (GS) which has been proposed to address the undersampling problem for dynamic MRI. We propose a modification to the conventional use of the model in order to reflect small hemodynamic signal changes typical in fMRI. Specifically, while realizing that having high model order is necessary to capture missing information, we found that it is not necessary to span all frequencies of GS basis functions uniformly. Instead, having $k$ -space and GS “sampling” trajectories covering low-frequencies uniformly while spanning high-frequencies sparsely, was observed to be an efficient strategy. The ability of the method over the conventional GS approach in improving resolution of functional images and activation maps while reducing undersampling ringing is demonstrated by simulations and experiments at $3$ T. Reduction in the readout time allowed an increase of statistical signal power as compared to the fully sampled acquisition. Unlike compressed sensing approaches, the proposed method is linear and hence has lower computational complexity. The method could prove useful for other imaging modalities where the signal change is smaller than the ba- eline component.

Beschreibung: Interbreath interval (IBI), the time interval between breaths, is an important measure used to analyze irregular breathing patterns in neonates. The discrete bursts of neural activity generate the IBI time series, which exhibits stochastic as well as deterministic dynamics. To quantify the irregularity of breathing, we propose a point process model of IBI using a comprehensive stochastic dynamic modeling framework. The IBIs of immature breathing patterns exhibit a long tail distribution and within a point process model, we have considered the lognormal distribution to represent the stochastic IBI characteristics. An autoregressive (AR) function is embedded within the model to capture the short-term IBI dynamics including abrupt IBI prolongations related to sporadic and periodic apneas that are common in neonates. We tested the utility of our paradigm for depicting the respiratory dynamics in neonatal rats and in preterm infants. Kolmogorov–Smirnov (KS) and independence tests reveal that the model accurately tracks the dynamic characteristics of the signals. In preterm infants, our model-derived indices of IBI instability strongly correlate with clinically derived indices of maturation. Our results validate a new class of algorithms, based on the point process theory, for defining instantaneous measures of breathing irregularity in neonates.

Beschreibung: Remote photoplethysmography (rPPG) enables contactless monitoring of the blood volume pulse using a regular camera. Recent research focused on improved motion robustness, but the proposed blind source separation techniques (BSS) in RGB color space show limited success. We present an analysis of the motion problem, from which far superior chrominance-based methods emerge. For a population of 117 stationary subjects, we show our methods to perform in 92% good agreement ( $pm hbox{1.96}sigma$ ) with contact PPG, with RMSE and standard deviation both a factor of 2 better than BSS-based methods. In a fitness setting using a simple spectral peak detector, the obtained pulse-rate for modest motion (bike) improves from 79% to 98% correct, and for vigorous motion (stepping) from less than 11% to more than 48% correct. We expect the greatly improved robustness to considerably widen the application scope of the technology.

Beschreibung: A system for capturing habitual, in-home gait measurements using an environmentally mounted depth camera, the Microsoft Kinect, is presented. Previous work evaluating the use of the Kinect sensor for in-home gait measurement in a lab setting has shown the potential of this approach. In this paper, a single Kinect sensor and computer were deployed in the apartments of older adults in an independent living facility for the purpose of continuous, in-home gait measurement. In addition, a monthly fall risk assessment protocol was conducted for each resident by a clinician, which included traditional tools such as the timed up a go and habitual gait speed tests. A probabilistic methodology for generating automated gait estimates over time for the residents of the apartments from the Kinect data is described, along with results from the apartments as compared to two of the traditionally measured fall risk assessment tools. Potential applications and future work are discussed.

Beschreibung: Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: $0.94 pm 0.08$ ) and “Euler characteristic” (AUC: $0.94 pm 0.07$ ), and GLCM-derived feature “Correlation” (AUC: $0.93 pm 0.07$ ). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.

Beschreibung: We have developed a pupil-corneal reflection method-based gaze detection system, which allows large head movements and achieves easy gaze calibration. This system contains two optical systems consisting of components such as a camera and a near-infrared light source attached to the camera. The light source has two concentric LED rings with different wavelengths. The inner and outer rings generate bright and dark pupil images, respectively. The pupils are detected from a difference image created by subtracting the bright and dark pupil images. The light source also generates the corneal reflection. The 3-D coordinates of the pupils are determined by the stereo matching method using two optical systems. The vector from the corneal reflection center to the pupil center in the camera image is determined as ${bm r}$ . The angle between the line of sight and the line passing through the pupil center and the camera (light source) is denoted as $theta$ . The relationship $theta = k leftvert {bm r} rightvert$ is assumed, where $k$ is a constant. The theory implies that head movement of the user is allowed and facilitates the gaze calibration procedure. In the automatic calibration method, $k$ is automatically determined while the user looks around on the PC screen without fixating on any specific calibration target. In the one-point calibration method, the user is asked to fixate on one calibration target at the PC screen in order to correct the difference between the optical and visual axes. In the two-point calibration method, in order to correct the nonlinear relationship between $theta$ and $leftvert {bm r} rightvert$ , the user is asked to fixate on two targets. The experimental results show that the three proposed calibration methods improve the precision of gaze detection step by step. In addition, the average gaze error in the visual angle is less than 1 $^circ$ for the seven head positions of the user.

Beschreibung: We present an encapsulation scheme that combines atomic layer deposited (ALD) Al $_{2}$ O $_{3}$ and Parylene C for the encapsulation of implantable devices. The encapsulation performances of combining alumina and Parylene C was compared to individual layers of Parylene C or alumina and the bilayer coating had superior encapsulation properties. The alumina–Parylene coated interdigitated electrodes (IDEs) soaked in PBS for up to nine months at temperatures from 37 to 80 °C for accelerated lifetime testing. For 52-nm alumina and 6-μm Parylene C, leakage current was ∼20 pA at 5 VDC, and the impedance was about 3.5 MΩ at 1 kHz with a phase near −87° from electrochemical impedance spectroscopy for samples soaked at 67 °C for equivalent lifetime of 72 months at 37 °C. The change of impedance during the whole soaking period (up to 70 months of equivalent soaking time at 37 °C) over 1 to 10 $^{6}$  Hz was within 5%. The stability of impedance indicated almost no degradation of the encapsulation. Bias voltage effect was studied by continuously applying 5 VDC, and it reduced the lifetime of Parylene coating by ∼75% while it showed no measurable effect on the bilayer coating. Lifetime of encapsulation of IDEs with topography generated by attaching a coil and surface mount device (SMD) capacitor was about half of that of planer IDEs. The stable long-term insulation impedance, low leakage current, and better lifetime under bias voltage and topography made this double-layer encapsulation very promising for chronic implantable devices.

Beschreibung: Muscle sympathetic nerve activity is a primary source of cardiovascular control in humans. Traditional analyses smooth away the fine temporal structure of the sympathetic recordings, limiting our understanding of sympathetic activation mechanisms. We use multifiber spike trains extracted from standard microneurography voltage trace to characterize the sympathetic spiking at rest and during sympathoexcitation. Our analysis corroborates known features of sympathetic activity, such as bursting behavior, cardiac rhythmicity, and long conduction delays. It also elucidates new features such as large heartbeat-to-heartbeat variability of firing rates and precise pattern of spiking within cardiac cycles. We find that at low firing rates, spikes occur uniformly throughout the cardiac cycle, but at higher rates, they tend to cluster in bursts around a particular latency. This latency shortens and the clusters tighten as the firing rates grow. Sympathoexcitation increases firing rates and shifts the burst latency later. Negative rate/latency correlation and the sympathoexcitatory shift suggest that spike production of the individual fibers contributes significantly to the control of the sympathetic bursts strength. Access to fine scale temporal information, more physiologically accurate description of nerve activity, and new hypotheses about the nervous outflow control establishes sympathetic spiking as a valuable tool for the cardiovascular research.

Beschreibung: In this paper, we report the development of a flexible base array of penetrating electrodes which can be used to interface with the spinal cord. A customizable and feasible fabrication protocol is described. The flexible base arrays were fabricated and implanted into surrogate cords which were elongated by 12%. The resulting strains were optically measured across the cord and compared to those associated with two types of electrodes arrays (one without a base and one with a rigid base connecting the electrodes). The deformation behavior of cords implanted with the flexible base arrays resembled the behavior of cords implanted with individual microwires that were not connected through a base. The results of the strain test were used to validate a 2-D finite element model. The validated model was used to assess the stresses induced by the electrodes of the three types of arrays on the cord, and to examine how various design parameters (thickness, base modulus, etc.,) impact the mechanical behavior of the electrode array. Rigid base arrays induced higher stresses on the cord than the flexible base arrays which in turn imposed higher stresses than the individual microwire implants. The developed flexible base array showed improvement over the rigid base array; however, its stiffness needs to be further reduced to emulate the mechanical behavior of individual microwire arrays without a base.

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

Beschreibung: The paper presents a new endoscope motion tracking method that is based on a novel external endoscope tracking device and our modified stochastic optimization method for boosting endoscopy navigation. We designed a novel tracking prototype where a 2-D motion sensor was introduced to directly measure the insertion–retreat linear motion and also the rotation of the endoscope. With our developed stochastic optimization method, which embeds traceable particle swarm optimization in the Condensation algorithm, a full six degrees-of-freedom endoscope pose (position and orientation) can be recovered from 2-D motion sensor measurements. Experiments were performed on a dynamic bronchial phantom with maximal simulated respiratory motion around 24.0 mm. The experimental results demonstrate that our proposed method provides a promising endoscope motion tracking approach with more effective and robust performance than several current available tracking techniques. The average tracking accuracy of the position improved from 6.5 to 3.3 mm, which further approaches the clinical requirement of 2.0 mm in practice.

Beschreibung: Radical prostatectomy is performed on approximately 40% of men with organ-confined prostate cancer. Pathologic information obtained from the prostatectomy specimen provides important prognostic information and guides recommendations for adjuvant treatment. The current pathology protocol in most centers involves primarily qualitative assessment. In this paper, we describe and evaluate our system for automatic prostate cancer detection and grading on hematoxylin & eosin-stained tissue images. Our approach is intended to address the dual challenges of large data size and the need for high-level tissue information about the locations and grades of tumors. Our system uses two stages of AdaBoost-based classification. The first provides high-level tissue component labeling of a superpixel image partitioning. The second uses the tissue component labeling to provide a classification of cancer versus noncancer, and low-grade versus high-grade cancer. We evaluated our system using 991 sub-images extracted from digital pathology images of 50 whole-mount tissue sections from 15 prostatectomy patients. We measured accuracies of 90% and 85% for the cancer versus noncancer and high-grade versus low-grade classification tasks, respectively. This system represents a first step toward automated cancer quantification on prostate digital histopathology imaging, which could pave the way for more accurately informed postprostatectomy patient care.

Beschreibung: We present DevStaR, an automated computer vision and machine learning system that provides rapid, accurate, and quantitative measurements of C. elegans embryonic viability in high-throughput (HTP) applications. A leading genetic model organism for the study of animal development and behavior, C. elegans is particularly amenable to HTP functional genomic analysis due to its small size and ease of cultivation, but the lack of efficient and quantitative methods to score phenotypes has become a major bottleneck. DevStaR addresses this challenge using a novel hierarchical object recognition machine that rapidly segments, classifies, and counts animals at each developmental stage in images of mixed-stage populations of C. elegans. Here, we describe the algorithmic design of the DevStaR system and demonstrate its performance in scoring image data acquired in HTP screens.

Beschreibung: Atrial fibrillation is an increasingly prevalent cardiovascular disease; changes in atrial structure and function induced by atrial fibrillation and its treatments are often spatially heterogeneous. However, spatial heterogeneity of function is difficult to assess with standard imaging techniques. This paper describes a method to assess global and regional mechanical function by combining cardiac magnetic resonance imaging and finite-element surface fitting. We used this fitted surface to derive measures of left atrial volume, regional motion, and spatial heterogeneity of motion in 23 subjects, including healthy volunteers and atrial fibrillation patients. We fit the surfaces using a Newton optimization scheme in under 1 min on a standard laptop, with a root mean square error of $2.3pm 0.5$ mm, less than 9% of the mean fitted radius, and an inter-operator variability of less than 10%. Fitted surfaces showed clear definition of the phases of left atrial motion (filling, passive emptying, active contraction) in both volume-time and regional radius-time curves. Averaged surfaces of healthy volunteers and atrial fibrillation patients provided evidence of substantial regional variation in both amount and timing of regional motion, indicating spatial heterogeneity of function, even in healthy adults.

Beschreibung: Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool capable of providing spatially localized maps of metabolite concentrations. Its utility, however, is often depreciated by spectral leakage artifacts resulting from low spatial resolution measurements through an effort to reduce acquisition times. Though model-based techniques can help circumvent these drawbacks, they require strong prior knowledge, and can introduce additional artifacts when the underlying models are inaccurate. We introduce a novel scheme in which a generative model is estimated from the raw MRSI data via a regularized variational framework that minimizes the model approximation error within a measurement-prescribed subspace. As additional a priori information, our approach relies only upon a measured field inhomogeneity map at high spatial resolution. We demonstrate the feasibility of our approach on both synthetic and experimental data.

Beschreibung: The absorbed energy distribution (AED) in X-ray imaging detectors is an important factor that affects both energy resolution and image quality through the Swank factor and detective quantum efficiency. In the diagnostic energy range (20–140 keV), escape of characteristic photons following photoelectric absorption and Compton scatter photons are primary sources of absorbed-energy dispersion in X-ray detectors. In this paper, we describe the development of an analytic model of the AED in compound X-ray detector materials, based on the cascaded-systems approach, that includes the effects of escape and reabsorption of characteristic and Compton-scatter photons. We derive analytic expressions for both semi-infinite slab and pixel geometries and validate our approach by Monte Carlo simulations. The analytic model provides the energy-dependent X-ray response function of arbitrary compound materials without time-consuming Monte Carlo simulations. We believe this model will be useful for correcting spectral distortion artifacts commonly observed in photon-counting applications and optimal design and development of novel X-ray detectors.

Beschreibung: We investigate an approach to evaluation of emission-tomography (ET) imaging systems used for region-of-interest (ROI) estimation tasks. In the evaluation we employ the concept of “emission counts” (EC), which are the number of events per voxel emitted during a scan. We use the reduction in posterior variance of ROI EC, compared to the prior ROI EC variance, as the metric of primary interest, which we call the “posterior variance reduction index” (PVRI). Systems that achieve a higher PVRI are considered superior to systems with lower PVRI. The approach is independent of the reconstruction method and is applicable to all photon-limited data types including list-mode data. We analyzed this approach using a model of 2-D tomography, and compared our results to the classical theory of tomographic sampling. We found that performance evaluations using the PVRI index were consistent with the classical theory. System evaluation based on EC posterior variance is an intuitively appealing and physically meaningful method that is useful for evaluation of system performance in ROI quantitation tasks.

Beschreibung: This paper presents a novel probabilistic voxel selection strategy for medical image registration in time-sensitive contexts, where the goal is aggressive voxel sampling (e.g., using less than 1% of the total number) while maintaining registration accuracy and low failure rate. We develop a Bayesian framework whereby, first, a voxel sampling probability field (VSPF) is built based on the uncertainty on the transformation parameters. We then describe a practical, multi-scale registration algorithm, where, at each optimization iteration, different voxel subsets are sampled based on the VSPF. The approach maximizes accuracy without committing to a particular fixed subset of voxels. The probabilistic sampling scheme developed is shown to manage the tradeoff between the robustness of traditional random voxel selection (by permitting more exploration) and the accuracy of fixed voxel selection (by permitting a greater proportion of informative voxels).

Beschreibung: Segmentation of the spinal column from computed tomography (CT) images is a preprocessing step for a range of image-guided interventions. One intervention that would benefit from accurate segmentation is spinal needle injection. Previous spinal segmentation techniques have primarily focused on identification and separate segmentation of each vertebra. Recently, statistical multi-object shape models have been introduced to extract common statistical characteristics between several anatomies. These models can be used for segmentation purposes because they are robust, accurate, and computationally tractable. In this paper, we develop a statistical multi-vertebrae shape+pose model and propose a novel registration-based technique to segment the CT images of spine. The multi-vertebrae statistical model captures the variations in shape and pose simultaneously, which reduces the number of registration parameters. We validate our technique in terms of accuracy and robustness of multi-vertebrae segmentation of CT images acquired from lumbar vertebrae of 32 subjects. The mean error of the proposed technique is below 2 mm, which is sufficient for many spinal needle injection procedures, such as facet joint injections.

Beschreibung: In this paper, we report on the first edition of the HEp-2 Cells Classification contest, held at the 2012 edition of the International Conference on Pattern Recognition, and focused on indirect immunofluorescence (IIF) image analysis. The IIF methodology is used to detect autoimmune diseases by searching for antibodies in the patient serum but, unfortunately, it is still a subjective method that depends too heavily on the experience and expertise of the physician. This has been the motivation behind the recent initial developments of computer aided diagnosis systems in this field. The contest aimed to bring together researchers interested in the performance evaluation of algorithms for IIF image analysis: 28 different recognition systems able to automatically recognize the staining pattern of cells within IIF images were tested on the same undisclosed dataset. In particular, the dataset takes into account the six staining patterns that occur most frequently in the daily diagnostic practice: centromere, nucleolar, homogeneous, fine speckled, coarse speckled, and cytoplasmic. In the paper, we briefly describe all the submitted methods, analyze the obtained results, and discuss the design choices conditioning the performance of each method.

Beschreibung: Tissue displacements required for mechanical property reconstruction in magnetic resonance elastography (MRE) are acquired in a magnetic resonance imaging (MRI) scanner, therefore, anatomical information is available from other imaging sequences. Despite its availability, few attempts to incorporate prior spatial information in the MRE reconstruction process have been reported. This paper implements and evaluates soft prior regularization (SPR), through which homogeneity in predefined spatial regions is enforced by a penalty term in a nonlinear inversion strategy. Phantom experiments and simulations show that when predefined regions are spatially accurate, recovered property values are stable for SPR weighting factors spanning several orders of magnitude, whereas inaccurate segmentation results in bias in the reconstructed properties that can be mitigated through proper choice of regularization weighting. The method was evaluated in vivo by estimating viscoelastic mechanical properties of frontal lobe gray and white matter for five repeated scans of a healthy volunteer. Segmentations of each tissue type were generated using automated software, and statistically significant differences between frontal lobe gray and white matter were found for both the storage modulus and loss modulus $(p 〈 10^{-6})$ . Provided homogeneous property assumptions are reasonable, SPR produces accurate quantitative property estimates for tissue structures which are finer than the resolution currently achievable with fully distributed MRE.

Beschreibung: Recent research has demonstrated that improved image segmentation can be achieved by multiple template fusion utilizing both label and intensity information. However, intensity weighted fusion approaches use local intensity similarity as a surrogate measure of local template quality for predicting target segmentation and do not seek to characterize template performance. This limits both the usefulness and accuracy of these techniques. Our work here was motivated by the observation that the local intensity similarity is a poor surrogate measure for direct comparison of the template image with the true image target segmentation. Although the true image target segmentation is not available, a high quality estimate can be inferred, and this in turn allows a principled estimate to be made of the local quality of each template at contributing to the target segmentation. We developed a fusion algorithm that uses probabilistic segmentations of the target image to simultaneously infer a reference standard segmentation of the target image and the local quality of each probabilistic segmentation. The concept of comparing templates to a hidden reference standard segmentation enables accurate assessments of the contribution of each template to inferring the target image segmentation to be made, and in practice leads to excellent target image segmentation. We have used the new algorithm for the multiple-template-based segmentation and parcellation of magnetic resonance images of the brain. Intensity and label map images of each one of the aligned templates are used to train a local Gaussian mixture model based classifier. Then, each classifier is used to compute the probabilistic segmentations of the target image. Finally, the generated probabilistic segmentations are fused together using the new fusion algorithm to obtain the segmentation of the target image. We evaluated our method in comparison to other state-of-the-art segmentation methods. We demonstrated that our new fusio- algorithm has higher segmentation performance than these methods.

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Beschreibung: The dc component of biological sources is difficult to measure with body surface electrodes. This is due to the existence of an electrical polarization in the interface between the electrode and the electrolyte. A system to acquire a dc component of the biological origin is proposed by means of differentiating the polarization potential from the biological one. The acquisition system is comprised of an instrumentation amplifier, an integrated semiconductor analog switch, and ordinary silver/silver-chloride body surface electrodes. Biopotentials, electrooculograms from the eyes, were noninvasively recorded by the system with the analog switch which was inserted between the electrodes to make the two inputs electrically open following them being shorted. Asymptotes of a triple exponential function were subsequently extracted by regression model analysis on the output signals that were acquired immediately after the switch opened. One of the distinctive asymptotes of the function showed that the magnitude which corresponded to eye movements stayed stable as long as the eyes were fixed. Analysis of the time constants involved in the acquisition system elucidated that the asymptote was the biological origin.

Beschreibung: Tissue microarrays (TMAs) facilitate the survey of very large numbers of tumors. However, the manual assessment of stained TMA sections constitutes a bottleneck in the pathologist’s work flow. This paper presents a computational pipeline for automatically classifying and scoring breast cancer TMA spots that have been subjected to nuclear immunostaining. Spots are classified based on a bag of visual words approach. Immunohistochemical scoring is performed by computing spot features reflecting the proportion of epithelial nuclei that are stained and the strength of that staining. These are then mapped onto an ordinal scale used by pathologists. Multilayer perceptron classifiers are compared with latent topic models and support vector machines for spot classification, and with Gaussian process ordinal regression and linear models for scoring. Intraobserver variation is also reported. The use of posterior entropy to identify uncertain cases is demonstrated. Evaluation is performed using TMA images stained for progesterone receptor.

Beschreibung: The removal of spatially correlated noise is an important step in processing multichannel recordings. Here, a technique termed the adaptive common average reference (ACAR) is presented as an effective and simple method for removing this noise. The ACAR is based on a combination of the well-known common average reference (CAR) and an adaptive noise canceling (ANC) filter. In a convergent process, the CAR provides a reference to an ANC filter, which in turn provides feedback to enhance the CAR. This method was effective on both simulated and real data, outperforming the standard CAR when the amplitude or polarity of the noise changes across channels. In many cases, the ACAR even outperformed independent component analysis. On 16 channels of simulated data, the ACAR was able to attenuate up to approximately 290 dB of noise and could improve signal quality if the original SNR was as high as 5 dB. With an original SNR of 0 dB, the ACAR improved signal quality with only two data channels and performance improved as the number of channels increased. It also performed well under many different conditions for the structure of the noise and signals. Analysis of contaminated electrocorticographic recordings further showed the effectiveness of the ACAR.

Beschreibung: This paper proposes a multimodal approach for vessel segmentation of macular optical coherence tomography (OCT) slices along with the fundus image. The method is comprised of two separate stages; the first step is 2-D segmentation of blood vessels in curvelet domain, enhanced by taking advantage of vessel information in crossing OCT slices (named feedback procedure), and improved by suppressing the false positives around the optic nerve head. The proposed method for vessel localization of OCT slices is also enhanced utilizing the fact that retinal nerve fiber layer becomes thicker in the presence of the blood vessels. The second stage of this method is axial localization of the vessels in OCT slices and 3-D reconstruction of the blood vessels. Twenty-four macular spectral 3-D OCT scans of 16 normal subjects were acquired using a Heidelberg HRA OCT scanner. Each dataset consisted of a scanning laser ophthalmoscopy (SLO) image and limited number of OCT scans with size of 496 × 512 (namely, for a data with 19 selected OCT slices, the whole data size was 496 × 512 × 19). The method is developed with least complicated algorithms and the results show considerable improvement in accuracy of vessel segmentation over similar methods to produce a local accuracy of 0.9632 in area of SLO, covered with OCT slices, and the overall accuracy of 0.9467 in the whole SLO image. The results are also demonstrative of a direct relation between the overall accuracy and percentage of SLO coverage by OCT slices.

Beschreibung: A brain–computer interface (BCI) based on steady-state visual-evoked potentials (SSVEP) has two difficulties: limitation of the number of commands and uneven probabilities of command execution. To address these problems, the present paper proposes a paradigm of BCI using frequency-modulated visual stimuli. The commands are translated into code words consisting of binary digits, to which visual stimuli with distinct frequencies are assigned. Frequencies of SSVEP are recognized to detect bits, and a command to be executed is determined from the sequence of detected bits. Experimental results show that the proposed paradigm achieves a reliable BCI with higher accuracies and balanced command executing probabilities.

Beschreibung: A method for brain monitoring based on measuring the electroencephalogram (EEG) from electrodes placed in-the-ear (ear-EEG) was recently proposed. The objective of this study is to further characterize the ear-EEG and perform a rigorous comparison against conventional on-scalp EEG. This is achieved for both auditory and visual evoked responses, over steady-state and transient paradigms, and across a population of subjects. The respective steady-state responses are evaluated in terms of signal-to-noise ratio and statistical significance, while the qualitative analysis of the transient responses is performed by considering grand averaged event-related potential (ERP) waveforms. The outcomes of this study demonstrate conclusively that the ear-EEG signals, in terms of the signal-to-noise ratio, are on par with conventional EEG recorded from electrodes placed over the temporal region.

Beschreibung: Increasing the freedom of communication using conventional row/column (RC) P300 paradigm by naive way (increasing matrix size) may deteriorate inherent distraction effect and interaction speed. In this paper, we propose a two-level predictive (TLP) paradigm by integrating a $3times 3$ two-level matrix paradigm with a statistical language model. The TLP paradigm is evaluated using offline and online data from ten healthy subjects. Significantly larger event-related potentials (ERPs) are evoked by the TLP paradigm compared with the classical $6times 6$ RC. During an online task (correctly spell an English sentence with 57 characters), accuracy and information transfer rate for the TLP are increased by 14.45% and 29.29%, respectively, when compared with the $6times 6$ RC. Time to complete the task is also decreased by 24.61% using TLP. In sharp contrast, an $8times 8$ RC (naive extension of the $6times 6$ RC) consumed $19.18%$ more time than the classical $6times 6$ RC. Furthermore, the statistical language model is also exploited to improve classification accuracy in a Bayesian approach. The proposed Bayesian fusion method is tested offline on data from the online spelling tasks. The results show its potential improvement on single-trial ERP classification.

Beschreibung: This paper presents a class of hydration monitor that uses ultrathin, stretchable sheets with arrays of embedded impedance sensors for precise measurement and spatially multiplexed mapping. The devices contain miniaturized capacitive electrodes arranged in a matrix format, capable of integration with skin in a conformal, intimate manner due to the overall skin-like physical properties. These “epidermal” systems noninvasively quantify regional variations in skin hydration, at uniform or variable skin depths. Experimental results demonstrate that the devices possess excellent uniformity, with favorable precision and accuracy. Theoretical models capture the underlying physics of the measurement and enable quantitative interpretation of the experimental results. These devices are appealing for applications ranging from skin care and dermatology, to cosmetology and health/wellness monitoring, with the additional potential for combined use with other classes of sensors for comprehensive, quantitative physiological assessment via the skin.

Beschreibung: The electroencephalography source estimation problem consists of inferring cortical activation from measurements of electrical potential taken on the scalp surface. This inverse problem is intrinsically ill-posed. In particular the dimensionality of cortical sources greatly exceeds the number of electrode measurements, and source estimation requires regularization to obtain a unique solution. In this work, we introduce a novel regularization function called cortical graph smoothing, which exploits knowledge of anatomical connectivity available from diffusion-weighted imaging. Given a weighted graph description of the anatomical connectivity of the brain, cortical graph smoothing penalizes the weighted sum of squares of differences of cortical activity across the graph edges, thus encouraging solutions with consistent activation across anatomically connected regions. We explore the performance of the cortical graph smoothing source estimates for analysis of the event related potential for simple motor tasks, and compare against the commonly used minimum norm, weighted minimum norm, LORETA and sLORETA source estimation methods. Evaluated over a series of 18 subjects, the proposed cortical graph smoothing method shows superior localization accuracy compared to the minimum norm method, and greater relative peak intensity than the other comparison methods.

Beschreibung: A novel framework for the classification of acute rejection versus nonrejection status of renal transplants from 2-D dynamic contrast-enhanced magnetic resonance imaging is proposed. The framework consists of four steps. First, kidney objects are segmented from adjacent structures with a level set deformable boundary guided by a stochastic speed function that accounts for a fourth-order Markov–Gibbs random field model of the kidney/background shape and appearance. Second, a Laplace-based nonrigid registration approach is used to account for local deformations caused by physiological effects. Namely, the target kidney object is deformed over closed, equispaced contours (iso-contours) to closely match the reference object. Next, the cortex is segmented as it is the functional kidney unit that is most affected by rejection. To characterize rejection, perfusion is estimated from contrast agent kinetics using empirical indexes, namely, the transient phase indexes (peak signal intensity, time-to-peak, and initial up-slope), and a steady-phase index defined as the average signal change during the slowly varying tissue phase of agent transit. We used a $k_n$ -nearest neighbor classifier to distinguish between acute rejection and nonrejection. Performance of our method was evaluated using the receiver operating characteristics (ROC). Experimental results in 50 subjects, using a combinatoric $k_n$ -classifier, correctly classified 92% of training subjects, 100% of the test subjects, and yielded an area under the ROC curve that approached the ideal value. Our proposed framework thus holds promise as a reliable noninvasive diagnostic tool.

Beschreibung: Longitudinal imaging studies are frequently used to investigate temporal changes in brain morphology and often require spatial correspondence between images achieved through image registration. Beside morphological changes, image intensity may also change over time, for example when studying brain maturation. However, such intensity changes are not accounted for in image similarity measures for standard image registration methods. Hence, 1) local similarity measures, 2) methods estimating intensity transformations between images, and 3) metamorphosis approaches have been developed to either achieve robustness with respect to intensity changes or to simultaneously capture spatial and intensity changes. For these methods, longitudinal intensity changes are not explicitly modeled and images are treated as independent static samples. Here, we propose a model-based image similarity measure for longitudinal image registration that estimates a temporal model of intensity change using all available images simultaneously.

Beschreibung: We present a new approach for the automated segmentation of synapses in image stacks acquired by electron microscopy (EM) that relies on image features specifically designed to take spatial context into account. These features are used to train a classifier that can effectively learn cues such as the presence of a nearby post-synaptic region. As a result, our algorithm successfully distinguishes synapses from the numerous other organelles that appear within an EM volume, including those whose local textural properties are relatively similar. Furthermore, as a by-product of the segmentation, our method flawlessly determines synaptic orientation, a crucial element in the interpretation of brain circuits. We evaluate our approach on three different datasets, compare it against the state-of-the-art in synapse segmentation and demonstrate our ability to reliably collect shape, density, and orientation statistics over hundreds of synapses.

Beschreibung: Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.

Beschreibung: The use of diffusion tensor imaging (DTI) for studying the human heart in vivo is very challenging due to cardiac motion. This paper assesses the effects of cardiac motion on the human myocardial fiber architecture. To this end, a model for analyzing the effects of cardiac motion on signal intensity is presented. A Monte-Carlo simulation based on polarized light imaging data is then performed to calculate the diffusion signals obtained by the displacement of water molecules, which generate diffusion weighted (DW) images. Rician noise and in vivo motion data obtained from DENSE acquisition are added to the simulated cardiac DW images to produce motion-induced datasets. An algorithm based on principal components analysis filtering and temporal maximum intensity projection (PCATMIP) is used to compensate for motion-induced signal loss. Diffusion tensor parameters derived from motion-reduced DW images are compared to those derived from the original simulated DW images. Finally, to assess cardiac motion effects on in vivo fiber architecture, in vivo cardiac DTI data processed by PCATMIP are compared to those obtained from one trigger delay (TD) or one single phase acquisition. The results showed that cardiac motion produced overestimated fractional anisotropy and mean diffusivity as well as a narrower range of fiber angles. The combined use of shifted TD acquisitions and postprocessing based on image registration and PCATMIP effectively improved the quality of in vivo DW images and subsequently, the measurement accuracy of fiber architecture properties. This suggests new solutions to the problems associated with obtaining in vivo human myocardial fiber architecture properties in clinical conditions.

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

Beschreibung: Electrical impedance tomography (EIT) is a medical imaging technique in which current is applied on electrodes on the surface of the body, the resulting voltage is measured, and an inverse problem is solved to recover the conductivity and/or permittivity in the interior. Images are then formed from the reconstructed conductivity and permittivity distributions. In the 2-D geometry, EIT is clinically useful for chest imaging. In this work, an implementation of a D-bar method for complex admittivities on a general 2-D domain is presented. In particular, reconstructions are computed on a chest-shaped domain for several realistic phantoms including a simulated pneumothorax, hyperinflation, and pleural effusion. The method demonstrates robustness in the presence of noise. Reconstructions from trigonometric and pairwise current injection patterns are included.

Beschreibung: This paper describes a task-based, information-theoretic approach to the assessment of image quality in diagnostic sonography. We expand the Kullback–Leibler divergence metric $J$ , which quantifies the diagnostic information contained within recorded radio-frequency echo signals, into a spatial-frequency integral comprised of two spectral components: one describes patient features for low-contrast diagnostic tasks and the other describes instrumentation properties. The latter quantity is the acquisition information spectrum (AIS), which measures the density of object information that an imaging system is able to transfer to the echo data at each spatial frequency. AIS is derived based on unique properties of acoustic scattering in tissues that generate object contrast. Predictions made by the $J$ integral expression were validated through Monte Carlo studies using echo-signal data from simulated lesions. Our analysis predicts the diagnostic performance of any sonographic system at specific diagnostic tasks based on engineering properties of the instrument that constitute image quality.

Beschreibung: This paper describes a new subspace-based algorithm for the identification of Hammerstein systems. It extends a previous approach which described the Hammerstein cascade by a state-space model and identified it with subspace methods that are fast and require little a priori knowledge. The resulting state-space models predict the system response well but have many redundant parameters and provide limited insight into the system since they depend on both the nonlinear and linear elements. This paper addresses these issues by reformulating the problem so that there are many fewer parameters and each parameter is related directly to either the linear dynamics or the static nonlinearity. Consequently, it is straightforward to construct the continuous-time Hammerstein models corresponding to the estimated state-space model. Simulation studies demonstrated that the new method performs better than other well-known methods in the nonideal conditions that prevail during practical experiments. Moreover, it accurately distinguished changes in the linear component from those in the static nonlinearity. The practical application of the new algorithm was demonstrated by applying it to experimental data from a study of the stretch reflex at the human ankle. Hammerstein models were estimated between the velocity of ankle perturbations and the EMG activity of triceps surae for voluntary contractions in the plantarflexing and dorsiflexion directions. The resulting models described the behavior well, displayed the expected unidirectional rate sensitivity, and revealed that both the gain of the linear element and the threshold of the nonlinear changed with contraction direction.

Beschreibung: Lung needle biopsy image classification is a critical task for computer-aided lung cancer diagnosis. In this study, a novel method, multimodal sparse representation-based classification (mSRC), is proposed for classifying lung needle biopsy images. In the data acquisition procedure of our method, the cell nuclei are automatically segmented from the images captured by needle biopsy specimens. Then, features of three modalities (shape, color, and texture) are extracted from the segmented cell nuclei. After this procedure, mSRC goes through a training phase and a testing phase. In the training phase, three discriminative subdictionaries corresponding to the shape, color, and texture information are jointly learned by a genetic algorithm guided multimodal dictionary learning approach. The dictionary learning aims to select the topmost discriminative samples and encourage large disagreement among different subdictionaries. In the testing phase, when a new image comes, a hierarchical fusion strategy is applied, which first predicts the labels of the cell nuclei by fusing three modalities, then predicts the label of the image by majority voting. Our method is evaluated on a real image set of 4372 cell nuclei regions segmented from 271 images. These cell nuclei regions can be divided into five classes: four cancerous classes (corresponding to four types of lung cancer) plus one normal class (no cancer). The results demonstrate that the multimodal information is important for lung needle biopsy image classification. Moreover, compared to several state-of-the-art methods (LapRLS, MCMI-AB, mcSVM, ESRC, KSRC), the proposed mSRC can achieve significant improvement (mean accuracy of $hbox{88.1}%$ , precision of $hbox{85.2}%$ , recall of $hbox{92.8}%$ , etc.), especially for classi- ying different cancerous types.

Beschreibung: We present a needle deflection estimation method to anticipate needle bending during insertion into deformable tissue. Using limited additional sensory information, our approach reduces the estimation error caused by uncertainties inherent in the conventional needle deflection estimation methods. We use Kalman filters to combine a kinematic needle deflection model with the position measurements of the base and the tip of the needle taken by electromagnetic (EM) trackers. One EM tracker is installed on the needle base and estimates the needle tip position indirectly using the kinematic needle deflection model. Another EM tracker is installed on the needle tip and estimates the needle tip position through direct, but noisy measurements. Kalman filters are then employed to fuse these two estimates in real time and provide a reliable estimate of the needle tip position, with reduced variance in the estimation error. We implemented this method to compensate for needle deflection during simulated needle insertions and performed sensitivity analysis for various conditions. At an insertion depth of 150 mm, we observed needle tip estimation error reductions in the range of 28% (from 1.8 to 1.3 mm) to 74% (from 4.8 to 1.2 mm), which demonstrates the effectiveness of our method, offering a clinically practical solution.

Beschreibung: Myoelectric control algorithms have the potential to detect an amputee's motion intent and allow the prosthetic to adapt to changes in walking mode. The development of a myoelectric walking mode classifier for transtibial amputees is outlined. Myoelectric signals from four muscles (tibialis anterior, medial gastrocnemius (MG), vastus lateralis, and biceps femoris) were recorded for five nonamputee subjects and five transtibial amputees over a variety of walking modes: level ground at three speeds, ramp ascent/descent, and stair ascent/descent. These signals were decomposed into relevant features (mean absolute value, variance, wavelength, number of slope sign changes, number of zero crossings) over three subwindows from the gait cycle and used to test the ability of classification algorithms for transtibial amputees using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Detection of all seven walking modes had an accuracy of 97.9% for the amputee group and 94.7% for the nonamputee group. Misclassifications occurred most frequently between different walking speeds due to the similar nature of the gait pattern. Stair ascent/descent had the best classification accuracy with 99.8% for the amputee group and 100.0% for the nonamputee group. Stability of the developed classifier was explored using an electrode shift disturbance for each muscle. Shifting the electrode placement of the MG had the most pronounced effect on the classification accuracy for both samples. No increase in classification accuracy was observed when using SVM compared to LDA for the current dataset.

Beschreibung: A real-time dual-mode ultrasound array (DMUA) system for imaging and therapy is described. The system utilizes a concave (40-mm radius of curvature) 3.5 MHz, 32 element array, and modular multichannel transmitter/receiver. The system is capable of operating in a variety of imaging and therapy modes (on transmit) and continuous receive on all array elements even during high-power operation. A signal chain consisting of field-programmable gate arrays and graphical processing units is used to enable real time, software-defined beamforming and image formation. Imaging data, from quality assurance phantoms as well as in vivo small- and large-animal models, are presented and discussed. Corresponding images obtained using a temporally-synchronized and spatially-aligned diagnostic probe confirm the DMUA’s ability to form anatomically-correct images with sufficient contrast in an extended field of view around its geometric center. In addition, high-frame rate DMUA data also demonstrate the feasibility of detection and localization of echo changes indicative of cavitation and/or tissue boiling during high-intensity focused ultrasound exposures with 45–50 dB dynamic range. The results also show that the axial and lateral resolution of the DMUA are consistent with its $f_{rm number}$ and bandwidth with well-behaved speckle cell characteristics. These results point the way to a theranostic DMUA system capable of quantitative imaging of tissue property changes with high specificity to lesion formation using focused ultrasound.

Beschreibung: In medical ultrasound imaging, lateral resolution is limited when using a fixed transmit focusing. Various synthetic aperture (SA) techniques, in which two-way dynamic focusing is enabled by utilizing prebeamformed radio-frequency (RF) data have been proposed for improving the spatial resolution. However, SA methods were not extensively evaluated in terms of their clinical performance. In this paper, a phantom and an in vivo evaluation of the SA method with bidirectional pixel-based focusing (BiPBF) is presented in comparison with the conventional beamforming. The performance of the proposed SA-BiPBF was assessed with a blind study and the established breast imaging–reporting and data system (BI-RADS), in addition to measuring contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). Prebeamformed RF data were acquired from a tissue mimicking phantom (Model 040, CIRS Inc., Norfolk, VA, USA) and from patients with breast lesions by using a commercial ultrasound scanning system with a linear array transducer equipped with a research package and parallel data acquisition system (SonixTouch, SonixDAQ, and L14-5/38, Ultrasonix Corp., Canada). In phantom and in vivo experiments, a default setting of a breast preset was applied (e.g., the center frequency of 10 MHz and acoustic output of MI = 0.66). In phantom experiment, the SA-BiPBF method showed higher CNR and SNR values compared to the conventional method (3.4 and 23.9 dB versus 3.1 and 15.8 dB, respectively). In addition, the lateral resolution and penetration depth were increased by 95.4% and 40.3%, respectively. Consistent with the phantom experiment, in the in vivo experiment with ten patients, the CNR value for the SA method was 3.3 $ pm$ 0.5 compared to 2.8 $ pm$ 0.8 for the conventional method. Similarly, the SNR values- with the SA-BiPBF and conventional methods were 34.0 $ pm$ 3.6 and 27.2 $ pm$ 3.4 dB, respectively. From the experiments, it was shown in side-by-side comparisons that the image quality of the SA-BiPBF method was considerably improved in both phantom and in vivo breast images. However, the SA-BiPBF image showed different features compared to the conventional one in the in vivo experiments. These features are resulting from the increased image quality of the SA-BiPBF method but are not always perceived as improvements by the radiologists.

Beschreibung: Prognosis and diagnosis of cardiac diseases frequently require quantitative evaluation of the ventricle volume, mass, and ejection fraction. The delineation of the myocardial wall is involved in all of these evaluations, which is a challenging task due to large variations in myocardial shapes and image quality. In this paper, we present an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ventricles are located sequentially, in which each ventricle is detected by first identifying the endocardium and then segmenting the epicardium. To this end, the endocardium is localized by utilizing its geometric features obtained on-line from a CT image. After that, a variational region-growing model is employed to extract the epicardium of the ventricles. In particular, the location of the endocardium of the left ventricle is determined via using an active contour model on the blood-pool surface. To localize the right ventricle, the active contour model is applied on a heart surface extracted based on the left ventricle segmentation result. The robustness and accuracy of the proposed approach is demonstrated by experimental results from 33 human and 12 pig CT images.

Beschreibung: Emerging targeted therapies have shown benefits such as less toxicity and higher effectiveness in specific types of cancer treatment; however, the accessibility of these advantages may rely on correct identification of suitable patients, which remains highly immature. We assume that copy number profiles, being accessible genomic data via microarray techniques, can provide useful information regarding drug response and shed light on personalized therapy. Based on the mechanism of action (MOA) of trastuzumab in the HER2 signaling pathway, a Bayesian network model in which copy number alterations (CNAs) serve as latent parents modifying signal transduction is applied. Two model parameters $M$ -score and $R$ -value which stand for the qualitative and quantitative effects of CNAs on drug effectiveness and are functions of conditional probabilities (CPs), are defined. An expectation–maximization (EM) algorithm is developed for estimating CPs, $M$ -scores, and $R$ -values from continuous measures, such as microarray data. We show through simulations that the EM algorithm can outperform classical threshold-based methods in the estimation of CPs and thereby provide improved performance for the detection of unfavorable CNAs. Several candidates of unfavorable CNAs to the trastuzumab therapy in breast cancer are provided in a real data example.

Beschreibung: In computed tomography of liver tumors there is often heterogeneous density, weak boundaries, and the liver tumors are surrounded by other abdominal structures with similar densities. These pose limitations to accurate the hepatic tumor segmentation. We propose a level set model incorporating likelihood energy with the edge energy. The minimization of the likelihood energy approximates the density distribution of the target and the multimodal density distribution of the background that can have multiple regions. In the edge energy formulation, our edge detector preserves the ramp associated with the edges for weak boundaries. We compared our approach to the Chan–Vese and the geodesic level set models and the manual segmentation performed by clinical experts. The Chan–Vese model was not successful in segmenting hepatic tumors and our model outperformed the geodesic level set model. Our results on 18 clinical datasets showed that our algorithm had a Jaccard distance error of 14.4 ± 5.3%, relative volume difference of –8.1 ± 2.1%, average surface distance of 2.4 ± 0.8 mm, RMS surface distance of 2.9 ± 0.7 mm, and the maximum surface distance of 7.2 ± 3.1 mm.

Beschreibung: Motion-compensated image reconstruction (MCIR) methods incorporate motion models to improve image quality in the presence of motion. MCIR methods differ in terms of how they use motion information and they have been well studied separately. However, there have been less theoretical comparisions of different MCIR methods. This paper compares the theoretical noise properties of three popular MCIR methods assuming known nonrigid motion. We show the relationship among three MCIR methods—motion-compensated temporal regularization (MTR), the parametric motion model (PMM), and post-reconstruction motion correction (PMC)—for penalized weighted least square cases. These analyses show that PMM and MTR are matrix-weighted sums of all registered image frames, while PMC is a scalar-weighted sum. We further investigate the noise properties of MCIR methods with Poisson models and quadratic regularizers by deriving accurate and fast variance prediction formulas using an “analytical approach.” These theoretical noise analyses show that the variances of PMM and MTR are lower than or comparable to the variance of PMC due to the statistical weighting. These analyses also facilitate comparisons of the noise properties of different MCIR methods, including the effects of different quadratic regularizers, the influence of the motion through its Jacobian determinant, and the effect of assuming that total activity is preserved. Two-dimensional positron emission tomography simulations demonstrate the theoretical results.

Beschreibung: Accurate understanding of equipment tracking error is essential for decision making in image guided surgery. For tools tracked using markers attached to a rigid body, existing error estimation methods use the assumption that the individual marker errors are independent random variables. This assumption is not valid for all tracking systems. This paper presents a method to estimate a more accurate tracking error function, consisting of a systematic and random component. The proposed method does not require detailed knowledge of the tracking system physics. Results from a pointer calibration are used to demonstrate that the proposed method provides a better match to observed results than the existing state of the art. A simulation of the pointer calibration process is then used to show that existing methods can underestimate the pointer calibration error by a factor of two. A further simulation of laparoscopic camera tracking is used to show that existing methods cannot model important variations in system performance due to the angular arrangement of the tracking markers. By arranging the markers such that the systematic errors are nearly identical for all markers, the rotational component of the tracking error can be reduced, resulting in a significant reduction in target tracking errors.

Beschreibung: We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in magnetic resonance imaging and to lung segmentation in full-body X-ray computed tomography. We evaluate our approach on a publicly available benchmark with competitive results.

Beschreibung: Elastography is a well-established imaging modality. While a number of studies aimed at evaluating the performance of elastographic techniques are retrievable in the literature, very little information is available on the effects that the presence of an underlying permeability contrast in the tissue may have on the resulting elastograms. Permeability is a fundamental tissue parameter, which characterizes the ease with which fluid can move within a tissue. This parameter plays a central role both biomechanically in the description of the temporal behavior of fluid-filled tissues and clinically in the development of a number of diagnostic and therapeutic modalities. In this paper, we present a simulation study that investigates selected elastographic image quality factors in nonhomogeneous materials, modeled as poroelastic media with different geometries and permeability contrasts. The results of this study indicate that the presence of an underlying permeability contrast may create a new contrast mechanism in the spatial and temporal distributions of the axial strains and the effective Poisson's ratios experienced by the tissue and as imaged by the corresponding elastograms. The effect of permeability on the elastographic image quality factors analyzed in this study was found to be a nonsymmetric function of the underlying mechanical contrast between background and target, the geometry of the material and the boundary conditions.

Beschreibung: Recent developments in medical imaging and advanced computer modeling simulations) now enable studies designed to correlate either simulated or measured “patient-specific” parameters with the natural history of intracranial aneurysm i.e., ruptured or unruptured. To achieve significance, however, these studies require rigorous comparison of large amounts of data from large numbers of aneurysms, many of which are quite dissimilar anatomically. In this study, we present a method that can likely facilitate such studies as its application could potentially simplify an objective comparison of surface-based parameters of interest such as wall shear stress and blood pressure using large multi-patient, multi-institutional data sets.

Beschreibung: Segmentation of the pulmonary lobes is relevant in clinical practice and particularly challenging for cases with severe diseases or incomplete fissures. In this work, an automated segmentation approach is presented that performs a marker-based watershed transformation on computed tomography (CT) scans to subdivide the lungs into lobes. A cost image for the watershed transformation is computed by combining information from fissures, bronchi, and pulmonary vessels. The lobar markers are calculated by an analysis of the automatically labeled bronchial tree. By integration of information from several anatomical structures the segmentation is made robust against incomplete fissures. For evaluation the method was compared to a recently published method on 20 CT scans with no or mild disease. The average distances to the reference segmentation were 0.69, 0.67, and 1.21 mm for the left major, right major, and right minor fissure, respectively. In addition the results were submitted to LOLA11, an international lung lobe segmentation challenge with publically available data including cases with severe diseases. The average distances to the reference for the 55 CT scans provided by LOLA11 were 0.98, 3.97, and 3.09 mm for the left major, right major, and right minor fissure. Moreover, an analysis of the relation between segmentation quality and fissure completeness showed that the method is robust against incomplete fissures.

Beschreibung: Viscoelastic properties of the myocardium are important for normal cardiac function and may be altered by disease. Thus, quantification of these properties may aid with evaluation of the health of the heart. Lamb wave dispersion ultrasound vibrometry (LDUV) is a shear wave-based method that uses wave velocity dispersion to measure the underlying viscoelastic material properties of soft tissue with plate-like geometries. We tested this method in eight pigs in an open-chest preparation. A mechanical actuator was used to create harmonic, propagating mechanical waves in the myocardial wall. The motion was tracked using a high frame rate acquisition sequence, typically 2500 Hz. The velocities of wave propagation were measured over the 50–400 Hz frequency range in 50 Hz increments. Data were acquired over several cardiac cycles. Dispersion curves were fit with a viscoelastic, anti-symmetric Lamb wave model to obtain estimates of the shear elasticity, $mu _{1}$ , and viscosity, $mu _{2}$ as defined by the Kelvin–Voigt rheological model. The sensitivity of the Lamb wave model was also studied using simulated data. We demonstrated that wave velocity measurements and Lamb wave theory allow one to estimate the variation of viscoelastic moduli of the myocardial walls in vivo throughout the course of the cardiac cycle.

Beschreibung: Positron emission tomographs (PETs) are currently almost exclusively designed as hybrid systems. The current standard is the PET/CT combination, while prototype PET/MRI systems are being studied by several research groups. One problem in these systems is that the transaxial field-of-view of the second system is smaller than that of the PET camera and does not provide complete attenuation data. Because this second system provides the image for PET attenuation and scatter correction, the smaller FOV causes truncation of the attenuation map, producing bias in the attenuation corrected activity image. In this paper, we propose a maximum-a-posteriori algorithm for estimating the missing part of the attenuation map from the PET emission data. The method is evaluated on five artificially truncated $^{18}{rm F mathchar"702D FDG PET/CT}$ studies, where it reduced the error on the reconstructed PET activities from 20% to less than 7%. The results on a PET/MRI patient study with $^{18}{rm F mathchar"702D FDG}$ are presented as well.

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

Beschreibung: Simultaneous imaging of multiple probes or biomarkers represents a critical step toward high specificity molecular imaging. In this work, we propose to utilize the element-specific nature of the X-ray fluorescence (XRF) signal for imaging multiple elements simultaneously (multiplexing) using XRF computed tomography (XFCT). A 5-mm-diameter pencil beam produced by a polychromatic X-ray source (150 kV, 20 mA) was used to stimulate emission of XRF photons from 2% (weight/volume) gold (Au), gadolinium (Gd), and barium (Ba) embedded within a water phantom. The phantom was translated and rotated relative to the stationary pencil beam in a first-generation CT geometry. The X-ray energy spectrum was collected for 18 s at each position using a cadmium telluride detector. The spectra were then used to isolate the K shell XRF peak and to generate sinograms for the three elements of interest. The distribution and concentration of the three elements were reconstructed with the iterative maximum likelihood expectation maximization algorithm. The linearity between the XFCT intensity and the concentrations of elements of interest was investigated. We found that measured XRF spectra showed sharp peaks characteristic of Au, Gd, and Ba. The narrow full-width at half-maximum (FWHM) of the peaks strongly supports the potential of XFCT for multiplexed imaging of Au, Gd, and Ba ( ${rm FWHM}_{{rm Au},{rm K}alpha 1}= 0.619 {rm keV}$ , ${rm FWHM}_{{rm Au},{rm K}alpha 2}=1.371 {rm keV}$ , ${rm FWHM}_{{rm Gd},{rm K}alpha}=1.297 {rm keV}$ , ${rm FWHM}_{{rm Gd},{rm K}beta}=0.974 {rm keV}$ , ${rm FWHM}_{{rm Ba},{rm K}alpha}=0.852 {rm keV}$ , and- ${rm FWHM}_{{rm Ba},{rm K}beta}=0.594 {rm keV}$ ). The distribution of Au, Gd, and Ba in the water phantom was clearly identifiable in the reconstructed XRF images. Our results showed linear relationships between the XRF intensity of each tested element and their concentrations ( ${rm R}^{2}_{rm Au}=0.944$ , ${rm R}_{rm Gd}^{2}=0.986$ , and ${rm R}_{rm Ba}^{2}=0.999$ ), suggesting that XFCT is capable of quantitative imaging. Finally, a transmission CT image was obtained to show the potential of the approach for providing attenuation correction and morphological information. In conclusion, XFCT is a promising modality for multiplexed imaging of high atomic number probes.

Beschreibung: A custom designed markerless tracking system was demonstrated to be applicable for positron emission tomography (PET) brain imaging. Precise head motion registration is crucial for accurate motion correction (MC) in PET imaging. State-of-the-art tracking systems applied with PET brain imaging rely on markers attached to the patient's head. The marker attachment is the main weakness of these systems. A healthy volunteer participating in a cigarette smoking study to image dopamine release was scanned twice for 2 h with $^{11}{rm C}$ -racolopride on the high resolution research tomograph (HRRT) PET scanner. Head motion was independently measured, with a commercial marker-based device and the proposed vision-based system. A list-mode event-by-event reconstruction algorithm using the detected motion was applied. A phantom study with hand-controlled continuous random motion was obtained. Motion was time-varying with long drift motions of up to 18 mm and regular step-wise motion of 1–6 mm. The evaluated measures were significantly better for motion-corrected images compared to no MC. The demonstrated system agreed with a commercial integrated system. Motion-corrected images were improved in contrast recovery of small structures.

Beschreibung: Robust anatomical correspondence detection is a key step in many medical image applications such as image registration and motion correction. In the computer vision field, graph matching techniques have emerged as a powerful approach for correspondence detection. By considering potential correspondences as graph nodes, graph edges can be used to measure the pairwise agreement between possible correspondences. In this paper, we present a novel, hierarchical graph matching method with sparsity constraint to further augment the power of conventional graph matching methods in establishing anatomical correspondences, especially for the cases of large inter-subject variations in medical applications. Specifically, we first propose to measure the pairwise agreement between potential correspondences along a sequence of intensity profiles which reduces the ambiguity in correspondence matching. We next introduce the concept of sparsity on the fuzziness of correspondences to suppress the distraction from misleading matches, which is very important for achieving the accurate, one-to-one correspondences. Finally, we integrate our graph matching method into a hierarchical correspondence matching framework, where we use multiple models to deal with the large inter-subject anatomical variations and gradually refine the correspondence matching results between the tentatively deformed model images and the underlying subject image. Evaluations on both synthetic data and public hand X-ray images indicate that the proposed hierarchical sparse graph matching method yields the best correspondence matching performance in terms of both accuracy and robustness when compared with several conventional graph matching methods.

Beschreibung: Tensor-based morphometry is a powerful tool for automatically computing longitudinal change in brain structure. Because of bias in images and in the algorithm itself, however, a penalty term and inverse consistency are needed to control the over-reporting of nonbiological change. These may force a tradeoff between the intrinsic sensitivity and specificity, potentially leading to an under-reporting of authentic biological change with time. We propose a new method incorporating prior information about tissue boundaries (where biological change is likely to exist) that aims to keep the robustness and specificity contributed by the penalty term and inverse consistency while maintaining localization and sensitivity. Results indicate that this method has improved sensitivity without increased noise. Thus it will have enhanced power to detect differences within normal aging and along the spectrum of cognitive impairment.

Beschreibung: Ultra-short TE (UTE) sequences with radial sampling make it possible to visualize tissues with very short T2 decay times. The UTE sequence acquires an echo signal from the central to the outer parts of k-space and is very sensitive to small trajectory errors. Therefore, k-space errors caused by imperfections in the gradient system performance, such as gradient delay and waveform distortion, must be corrected. During normal clinical use, these errors must be corrected to account for any gradient strength, or image obliquity. Because of time limitation on clinical examination, a simple, robust, and time-efficient correction method for use with UTE is needed. We demonstrated image degradation due to k-space errors by simulation and found that uncontrolled gradient time delays were the dominant cause of image degradation. They could be corrected by using a pre-scan calibration that works by comparison of half and full echo signals. Further improvements in image quality were achieved by using a one-time calibration of gradient waveform approximations that were built from multiple exponential functions and were used during image reconstruction. We have developed a robust UTE correction method that consists of a gradient waveform approximation that follows a short pre-scan for estimating gradient time delay errors.

Beschreibung: We examine a maximum-a-posteriori method for estimating the primary interaction position of gamma rays with multiple interaction sites (hits) in a monolithic detector. In assessing the performance of a multiple-hit estimator over that of a conventional one-hit estimator, we consider a few different detector and readout configurations of a 50-mm-wide square cerium-doped lutetium oxyorthosilicate block. For this study, we use simulated data from SCOUT, a Monte-Carlo tool for photon tracking and modeling scintillation- camera output. With this tool, we determine estimate bias and variance for a multiple-hit estimator and compare these with similar metrics for a one-hit maximum-likelihood estimator, which assumes full energy deposition in one hit. We also examine the effect of event filtering on these metrics; for this purpose, we use a likelihood threshold to reject signals that are not likely to have been produced under the assumed likelihood model. Depending on detector design, we observe a 1%–12% improvement of intrinsic resolution for a 1-or-2-hit estimator as compared with a 1-hit estimator. We also observe improved differentiation of photopeak events using a 1-or-2-hit estimator as compared with the 1-hit estimator; more than 6% of photopeak events that were rejected by likelihood filtering for the 1-hit estimator were accurately identified as photopeak events and positioned without loss of resolution by a 1-or-2-hit estimator; for PET, this equates to at least a 12% improvement in coincidence-detection efficiency with likelihood filtering applied.

Beschreibung: Thermoacoustic tomography (TAT) is a novel, non-invasive medical imaging technique but has encountered obstacles in imaging through the cranium. In this paper we present two methods for transcranial TAT: Kirchhoff migration (KM) and reverse-time migration (RTM). The two methods' imaging qualities are verified and compared based on both synthetic and experimental data. RTM proves to have better velocity variance and imaging quality, and little noise with spatial aliasing. RTM is a promising approach for achieving transcranial TAT in further studies.

Beschreibung: A novel online method based on the symmetry property of the sum of projections (SOP) is proposed to obtain the geometric parameters in cone-beam computed tomography (CBCT). This method requires no calibration phantom and can be used in circular trajectory CBCT with arbitrary cone angles. An objective function is deduced to illustrate the dependence of the symmetry of SOP on geometric parameters, which will converge to its minimum when the geometric parameters achieve their true values. Thus, by minimizing the objective function, we can obtain the geometric parameters for image reconstruction. To validate this method, numerical phantom studies with different noise levels are simulated. The results show that our method is insensitive to the noise and can determine the skew (in-plane rotation angle of the detector), the roll (rotation angle around the projection of the rotation axis on the detector), and the rotation axis with high accuracy, while the mid-plane and source-to-detector distance will be obtained with slightly lower accuracy. However, our simulation studies validate that the errors of the latter two parameters brought by our method will hardly degrade the quality of reconstructed images. The small animal studies show that our method is able to deal with arbitrary imaging objects. In addition, the results of the reconstructed images in different slices demonstrate that we have achieved comparable image quality in the reconstructions as some offline methods.

Beschreibung: A novel splat feature classification method is presented with application to retinal hemorrhage detection in fundus images. Reliable detection of retinal hemorrhages is important in the development of automated screening systems which can be translated into practice. Under our supervised approach, retinal color images are partitioned into nonoverlapping segments covering the entire image. Each segment, i.e., splat, contains pixels with similar color and spatial location. A set of features is extracted from each splat to describe its characteristics relative to its surroundings, employing responses from a variety of filter bank, interactions with neighboring splats, and shape and texture information. An optimal subset of splat features is selected by a filter approach followed by a wrapper approach. A classifier is trained with splat-based expert annotations and evaluated on the publicly available Messidor dataset. An area under the receiver operating characteristic curve of 0.96 is achieved at the splat level and 0.87 at the image level. While we are focused on retinal hemorrhage detection, our approach has potential to be applied to other object detection tasks.

Beschreibung: In this paper, we present a tubular structure segmentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient computed tomography angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert.

Beschreibung: Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary evidence, large object deformations, and mutual influence between adjacent objects. This paper reports a novel approach to multi-object segmentation that incorporates both shape and context prior knowledge in a 3-D graph-theoretic framework to help overcome the stated challenges. We employ an arc-based graph representation to incorporate a wide spectrum of prior information through pair-wise energy terms. In particular, a shape-prior term is used to penalize local shape changes and a context-prior term is used to penalize local surface-distance changes from a model of the expected shape and surface distances, respectively. The globally optimal solution for multiple surfaces is obtained by computing a maximum flow in a low-order polynomial time. The proposed method was validated on intraretinal layer segmentation of optical coherence tomography images and demonstrated statistically significant improvement of segmentation accuracy compared to our earlier graph-search method that was not utilizing shape and context priors. The mean unsigned surface positioning errors obtained by the conventional graph-search approach ( $6.30 pm 1.58$ $mu$ m) was improved to $5.14pm 0.99$ $mu$ m when employing our new method with shape and context priors.

Beschreibung: Speckle tracking methods refer to motion tracking methods based on speckle patterns in ultrasound images. They are commonly used in ultrasound based elasticity imaging techniques to reveal mechanical properties of tissues for clinical diagnosis. In speckle tracking, feature motion decorrelation exists when speckle patterns are not identical before and after tissue motion and deformation. Feature motion decorrelation violates the underlying assumption of most speckle tracking methods. Consequently, the estimation accuracy of current methods is greatly limited. In this paper, two types of speckle pattern variations, the geometric transformation and the intensity change of speckle patterns, are studied. We show that a coupled filtering method is able to compensate for both types of variations. It provides accurate strain estimations even when tissue deformation or rotation is extremely large. We also show that in most cases, an affine warping method that only compensates for the geometric transformation is able to achieve a similar performance as the coupled filtering method. Feature motion decorrelation in B-mode images is also studied. Finally, we show that in typical elastography studies, speckle tracking methods without modeling local shearing or rotation will fail when tissue deformation is large.

Beschreibung: We acquire the first experimental 3-D tomographic images with magnetic particle imaging (MPI) using projection reconstruction methodology, which is similar to algorithms employed in X-ray computed tomography. The primary advantage of projection reconstruction methods is an order of magnitude increase in signal-to-noise ratio (SNR) due to averaging. We first derive the point spread function, resolution, number of projections required, and the SNR gain in projection reconstruction MPI. We then design and construct the first scanner capable of gathering the necessary data for nonaliased projection reconstruction and experimentally verify our mathematical predictions. We demonstrate that filtered backprojection in MPI is experimentally feasible and illustrate the SNR and resolution improvements with projection reconstruction. Finally, we show that MPI is capable of producing three dimensional imaging volumes in both phantoms and postmortem mice.

Beschreibung: We present a novel framework for estimating the 3D poses and shapes of the carpal bones from single view fluoroscopic sequences. A hybrid statistical model representing both the pose and shape variation of the carpal bones is built, based on a number of 3D CT data sets obtained from different subjects at different poses. Given a fluoroscopic sequence, the wrist pose, carpal bone pose and bone shapes are estimated iteratively by matching the statistical model with the 2D images. A specially designed cost function enables smoothed parameter estimation across frames and constrains local bone pose with a penalty term. We have evaluated the proposed method on both simulated data and real fluoroscopic sequences and demonstrated that the relative poses of carpal bones can be accurately estimated. One condition that may be assessed using this measurement is dissociation, where the distance between the bones is larger than normal. Scaphoid–Lunate dissociation is one of the most common of these. The error of the measured 3D Scaphoid–Lunate distances were ${0.75}pm{0.50}~{rm mm}$ for simulated data (25 subjects) and ${0.93}pm{0.47}~{rm mm}$ for real data (15 subjects). We also propose a method for constructing a “standard” pathology measurement tool for automatically detecting Scaphoid–Lunate dissociation conditions, based on single-view fluoroscopic sequences. For the simulated data, it produced 100% sensitivity and specificity. For the real data, it achieved 83% sensitivity and 78% specificity.

Beschreibung: Compensating for the collimator-detector response (CDR) in SPECT is important for accurate quantification. The CDR consists of both a geometric response and a septal penetration and collimator scatter response. The geometric response can be modeled analytically and is often used for modeling the whole CDR if the geometric response dominates. However, for radionuclides that emit medium or high-energy photons such as I-131, the septal penetration and collimator scatter response is significant and its modeling in the CDR correction is important for accurate quantification. There are two main methods for modeling the depth-dependent CDR so as to include both the geometric response and the septal penetration and collimator scatter response. One is to fit a Gaussian plus exponential function that is rotationally invariant to the measured point source response at several source-detector distances. However, a rotationally-invariant exponential function cannot represent the star-shaped septal penetration tails in detail. Another is to perform Monte-Carlo (MC) simulations to generate the depth-dependent point spread functions (PSFs) for all necessary distances. However, MC simulations, which require careful modeling of the SPECT detector components, can be challenging and accurate results may not be available for all of the different SPECT scanners in clinics. In this paper, we propose an alternative approach to CDR modeling. We use a Gaussian function plus a 2-D B-spline PSF template and fit the model to measurements of an I-131 point source at several distances. The proposed PSF-template-based approach is nearly non-parametric, captures the characteristics of the septal penetration tails, and minimizes the difference between the fitted and measured CDR at the distances of interest. The new model is applied to I-131 SPECT reconstructions of experimental phantom measurements, a patient study, and a MC patient simulation study employing the XCAT phantom. The proposed model yi- lds up to a 16.5 and 10.8% higher recovery coefficient compared to the results with the conventional Gaussian model and the Gaussian plus exponential model, respectively.

Beschreibung: Despite the availability of multiple solutions for assessing myocardial strain by ultrasound, little is currently known about the relative performance of the different methods. In this study, we sought to contrast two strain estimation techniques directly (speckle tracking and elastic registration) in an in vivo setting by comparing both to a gold standard reference measurement. In five open-chest sheep instrumented with ultrasonic microcrystals, 2-D images were acquired with a GE Vivid7 ultrasound system. Radial $(epsilon_{RR})$ , longitudinal $(epsilon_{LL})$ , and circumferential strain $(epsilon_{CC})$ were estimated during four inotropic stages: at rest, during esmolol and dobutamine infusion, and during acute ischemia. The correlation of the end-systolic strain values of a well-validated speckle tracking approach and an elastic registration method against sonomicrometry were comparable for $epsilon_{LL}$ ( ${ r}=0.70$ versus ${ r}=0.61$ , respectively; ${ p}=0.32$ ) and $epsilon_{CC}$ ( ${ r}=0.73$ versus ${ r}=0.80$ respectively; ${ p}=0.31$ ). However, the elastic registration method performed considerably better for $epsilon_{RR}$ ( ${ r}=0.64$ versus ${ r}=0.85$ respectively; ${ p}=0.09$ ). Moreover, the bias and limits of agreement with respect to the reference strain estimates were statistically significantly smaller in this direction $({ p}〈0.001)$ . This could be related to regularization which is imposed during the motion estimation process as opposed to an a posteriori regularization step in the speckle tracking method. Whether one method outperforms the other in detecting dysfunctional regions remains the topic of future research.

Beschreibung: Cancer causes deviations in the distribution of cells, leading to changes in biological structures that they form. Correct localization and characterization of these structures are crucial for accurate cancer diagnosis and grading. In this paper, we introduce an effective hybrid model that employs both structural and statistical pattern recognition techniques to locate and characterize the biological structures in a tissue image for tissue quantification. To this end, this hybrid model defines an attributed graph for a tissue image and a set of query graphs as a reference to the normal biological structure. It then locates key regions that are most similar to a normal biological structure by searching the query graphs over the entire tissue graph. Unlike conventional approaches, this hybrid model quantifies the located key regions with two different types of features extracted using structural and statistical techniques. The first type includes embedding of graph edit distances to the query graphs whereas the second one comprises textural features of the key regions. Working with colon tissue images, our experiments demonstrate that the proposed hybrid model leads to higher classification accuracies, compared against the conventional approaches that use only statistical techniques for tissue quantification.

Beschreibung: Iterative image reconstruction with sparsity-exploiting methods, such as total variation (TV) minimization, investigated in compressive sensing claim potentially large reductions in sampling requirements. Quantifying this claim for computed tomography (CT) is nontrivial, because both full sampling in the discrete-to-discrete imaging model and the reduction in sampling admitted by sparsity-exploiting methods are ill-defined. The present article proposes definitions of full sampling by introducing four sufficient-sampling conditions (SSCs). The SSCs are based on the condition number of the system matrix of a linear imaging model and address invertibility and stability. In the example application of breast CT, the SSCs are used as reference points of full sampling for quantifying the undersampling admitted by reconstruction through TV-minimization. In numerical simulations, factors affecting admissible undersampling are studied. Differences between few-view and few-detector bin reconstruction as well as a relation between object sparsity and admitted undersampling are quantified.

Beschreibung: A method for the automatic detection of microaneurysms (MAs) in color retinal images is proposed in this paper. The recognition of MAs is an essential step in the diagnosis and grading of diabetic retinopathy. The proposed method realizes MA detection through the analysis of directional cross-section profiles centered on the local maximum pixels of the preprocessed image. Peak detection is applied on each profile, and a set of attributes regarding the size, height, and shape of the peak are calculated subsequently. The statistical measures of these attribute values as the orientation of the cross-section changes constitute the feature set that is used in a naïve Bayes classification to exclude spurious candidates. We give a formula for the final score of the remaining candidates, which can be thresholded further for a binary output. The proposed method has been tested in the Retinopathy Online Challenge, where it proved to be competitive with the state-of-the-art approaches. We also present the experimental results for a private image set using the same classifier setup.

Beschreibung: Routine clinical use of 2D-3D registration algorithms for Image Guided Surgery remains limited. A key aspect for routine clinical use of this technology is its degree of automation, i.e., the amount of necessary knowledgeable interaction between the clinicians and the registration system. Current image-based registration approaches usually require knowledgeable manual interaction during two stages: for initial pose estimation and for verification of produced results. We propose four novel techniques, particularly suited to vertebra-based registration systems, which can significantly automate both of the above stages. Two of these techniques are based upon the intraoperative “insertion” of a virtual fiducial marker into the preoperative data. The remaining two techniques use the final registration similarity value between multiple CT vertebrae and a single fluoroscopy vertebra. The proposed methods were evaluated with data from 31 operations (31 CT scans, 419 fluoroscopy images). Results show these methods can remove the need for manual vertebra identification during initial pose estimation, and were also very effective for result verification, producing a combined true positive rate of 100% and false positive rate equal to zero. This large decrease in required knowledgeable interaction is an important contribution aiming to enable more widespread use of 2D-3D registration technology.

Beschreibung: The finite element method is commonly used to model tissue deformation in order to solve for unknown parameters in the inverse problem of viscoelasticity. Typically, a (regular-grid) structured mesh is used since the internal geometry of the domain to be identified is not known a priori. In this work, the generation of problem-specific meshes is studied and such meshes are shown to significantly improve inverse-problem elastic parameter reconstruction. Improved meshes are generated from axial strain images, which provide an approximation to the underlying structure, using an optimization-based mesh adaptation approach. Such strain-based adapted meshes fit the underlying geometry even at coarse mesh resolutions, therefore improving the effective resolution of the reconstruction at a given mesh size/complexity. Elasticity reconstructions are then performed iteratively using the reflective trust-region method for optimizing the fit between estimated and observed displacements. This approach is studied for Young's modulus reconstruction at various mesh resolutions through simulations, yielding 40%–72% decrease in root-mean-square reconstruction error and 4–52 times improvement in contrast-to-noise ratio in simulations of a numerical phantom with a circular inclusion. A noise study indicates that conventional structured meshes with no noise perform considerably worse than the proposed adapted meshes with noise levels up to 20% of the compression amplitude. A phantom study and preliminary in vivo results from a breast tumor case confirm the benefit of the proposed technique. Not only conventional axial strain images but also other elasticity approximations can be used to adapt meshes. This is demonstrated on images generated by combining axial strain and axial-shear strain, which enhances lateral image contrast in particular settings, consequently further improving mesh-adapted reconstructions.

Beschreibung: In this paper, we propose a new prostate computed tomography (CT) segmentation method for image guided radiation therapy. The main contributions of our method lie in the following aspects. 1) Instead of using voxel intensity information alone, patch-based representation in the discriminative feature space with logistic sparse LASSO is used as anatomical signature to deal with low contrast problem in prostate CT images. 2) Based on the proposed patch-based signature, a new multi-atlases label fusion method formulated under sparse representation framework is designed to segment prostate in the new treatment images, with guidance from the previous segmented images of the same patient. This method estimates the prostate likelihood of each voxel in the new treatment image from its nearby candidate voxels in the previous segmented images, based on the nonlocal mean principle and sparsity constraint. 3) A hierarchical labeling strategy is further designed to perform label fusion, where voxels with high confidence are first labeled for providing useful context information in the same image for aiding the labeling of the remaining voxels. 4) An online update mechanism is finally adopted to progressively collect more patient-specific information from newly segmented treatment images of the same patient, for adaptive and more accurate segmentation. The proposed method has been extensively evaluated on a prostate CT image database consisting of 24 patients where each patient has more than 10 treatment images, and further compared with several state-of-the-art prostate CT segmentation algorithms using various evaluation metrics. Experimental results demonstrate that the proposed method consistently achieves higher segmentation accuracy than any other methods under comparison.

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