Journal Articles

IEEE Transactions on Medical Imaging information for authors

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Sparse Signal Recovery Methods for Multiplexing PET Detector Readout

Nuclear medicine imaging detectors are commonly multiplexed to reduce the number of readout channels. Because the underlying detector signals have a sparse representation, sparse recovery methods such as compressed sensing may be used to develop new multiplexing schemes. Random methods may be used to create sensing matrices that satisfy the restricted isometry property. However, the restricted isometry property provides little guidance for developing multiplexing networks with good signal-to-noise recovery capability. In this work, we describe compressed sensing using a maximum likelihood framework and develop a new method for constructing multiplexing (sensing) matrices that can recover signals more accurately in a mean square error sense compared to sensing matrices constructed by random construction methods. Signals can then be recovered by maximum likelihood estimation constrained to the support recovered by either greedy $ {ell _{0}} $ iterative algorithms or $ {ell _{1}} $-norm minimization techniques. We show that this new method for constructing and decoding sensing matrices recovers signals with 4%–110% higher SNR than random Gaussian sensing matrices, up to 100% higher SNR than partial DCT sensing matrices 50%–2400% higher SNR than cross-strip multiplexing, and 22%–210% higher SNR than Anger multiplexing for photoelectric events. Read more

IEEE Transactions on Medical Imaging publication information

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A Piecewise Monotone Subgradient Algorithm for Accurate -TV Based Registration of Physical Slices With Discontinuities in Microscopy

Image registration tasks are often formulated in terms of minimization of a functional consisting of a data fidelity term penalizing the mismatch between the reference and the target image, and a term enforcing smoothness of shift between neighboring pairs of pixels (a min-sum problem). Most methods for deformable image registration use some form of interpolation between matching control points. The interpolation makes it impossible to account for isolated discontinuities in the deformation field that may appear, e.g., when a physical slice of a microscopy specimen is ruptured by the cutting tool. For registration of neighboring physical slices of microscopy specimens with discontinuities, Janácek proposed an ${rm L}^{1}$-distance data fidelity term and a total variation (TV) smoothness term, and used a graph-cut (GC) based iterative steepest descent algorithm for minimization. The ${rm L}^{1}$-TV functional is nonconvex; hence a steepest descent algorithm is not guaranteed to converge to the global minimum. Schlesinger presented transformation of max-sum problems to minimization of a dual quantity called problem power, which is—contrary to the original max-sum functional—convex. Based on Schlesinger's solution to max-sum problems we developed an algorithm for ${rm L}^{1}$-TV minimization by iterative multi-label steepest descent minimization of the convex dual problem. For Schlesinger's subgradient algorithm we proposed a novel step control heuristics that considerably enhances both speed and accuracy compared with standard step size strategies for subgradient methods. It is shown experimentally that our subgradient scheme achieves consistently better image registration than GC in terms of lower values both of the composite ${rm L}^{1}$-TV functional, and of its components, i.e., the ${rm L}^{1}$ distance of the images and the transformation smoothness TV, and yields visually acceptable results even in cases where the GC based algorithm fails. The new algorithm allows easy parallelization and can thus be sped up by running on multi-core graphic processing units. Read more

Fast Joint Detection-Estimation of Evoked Brain Activity in Event-Related fMRI Using a Variational Approach

In standard within-subject analyses of event-related functional magnetic resonance imaging (fMRI) data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the so-called region-based joint detection-estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian model. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to sample the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a variational expectation-maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows automatic fine-tuning of spatial regularization parameters. It provides a new algorithm that exhibits interesting properties in terms of estimation error and computational cost compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model misspecification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery. Read more

Distributed MLEM: An Iterative Tomographic Image Reconstruction Algorithm for Distributed Memory Architectures

The processing speed for positron emission tomography (PET) image reconstruction has been greatly improved in recent years by simply dividing the workload to multiple processors of a graphics processing unit (GPU). However, if this strategy is generalized to a multi-GPU cluster, the processing speed does not improve linearly with the number of GPUs. This is because large data transfer is required between the GPUs after each iteration, effectively reducing the parallelism. This paper proposes a novel approach to reformulate the maximum likelihood expectation maximization (MLEM) algorithm so that it can scale up to many GPU nodes with less frequent inter-node communication. While being mathematically different, the new algorithm maximizes the same convex likelihood function as MLEM, thus converges to the same solution. Experiments on a multi-GPU cluster demonstrate the effectiveness of the proposed approach. Read more

Dynamic 3-D Visualization of Vocal Tract Shaping During Speech

Noninvasive imaging is widely used in speech research as a means to investigate the shaping and dynamics of the vocal tract during speech production. 3-D dynamic MRI would be a major advance, as it would provide 3-D dynamic visualization of the entire vocal tract. We present a novel method for the creation of 3-D dynamic movies of vocal tract shaping based on the acquisition of 2-D dynamic data from parallel slices and temporal alignment of the image sequences using audio information. Multiple sagittal 2-D real-time movies with synchronized audio recordings are acquired for English vowel-consonant-vowel stimuli /ala/, /a$hbox{ ?? }$a/, /asa/, and /a$! smallint !$a/. Audio data are aligned using mel-frequency cepstral coefficients (MFCC) extracted from windowed intervals of the speech signal. Sagittal image sequences acquired from all slices are then aligned using dynamic time warping (DTW). The aligned image sequences enable dynamic 3-D visualization by creating synthesized movies of the moving airway in the coronal planes, visualizing desired tissue surfaces and tube-shaped vocal tract airway after manual segmentation of targeted articulators and smoothing. The resulting volumes allow for dynamic 3-D visualization of salient aspects of lingual articulation, including the formation of tongue grooves and sublingual cavities, with a temporal resolution of 78 ms. Read more

Table of Contents

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Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions

Irregular streaks are important clues for Melanoma (a potentially fatal form of skin cancer) diagnosis using dermoscopy images. This paper extends our previous algorithm to identify the absence or presence of streaks in a skin lesions, by further analyzing the appearance of detected streak lines, and performing a three-way classification for streaks, Absent, Regular, and Irregular, in a pigmented skin lesion. In addition, the directional pattern of detected lines is analyzed to extract their orientation features in order to detect the underlying pattern. The method uses a graphical representation to model the geometric pattern of valid streaks and the distribution and coverage of the structure. Using these proposed features of the valid streaks along with the color and texture features of the entire lesion, an accuracy of 76.1% and weighted average area under ROC curve (AUC) of 85% is achieved for classifying dermoscopy images into streaks Absent, Regular, or Irregular on 945 images compiled from atlases and the internet without any exclusion criteria. This challenging dataset is the largest validation dataset for streaks detection and classification published to date. The data set has also been applied to the two-class sub-problems of Absent/Present classification (accuracy of 78.3% with AUC of 83.2%) and to Regular/Irregular classification (accuracy 83.6% with AUC of 88.9%). When the method was tested on a cleaned subset of 300 images randomly selected from the 945 images, the AUC increased to 91.8%, 93.2% and 90.9% for the Absent/Regular/Irregular, Absent/Present, and Regular/Irregular problems, respectively. Read more

Registration of Coronary CTA and Monoplane X-Ray Angiography

A method for registering preoperative $3{rm D}+{rm t}$ coronary CTA with intraoperative monoplane $2{rm D}+{rm t}$ X-ray angiography images is proposed to improve image guidance during minimally invasive coronary interventions. The method uses a patient-specific dynamic coronary model, which is derived from the CTA scan by centerline extraction and motion estimation. The dynamic coronary model is registered with the $2{rm D}+{rm t}$ X-ray sequence, considering multiple X-ray time points concurrently, while taking breathing induced motion into account. Evaluation was performed on 26 datasets of 17 patients by comparing projected model centerlines with manually annotated centerlines in the X-ray images. The proposed $3{rm D}+{rm t}/2{rm D}+{rm t}$ registration method performed better than a 3D/2D registration method with respect to the accuracy and especially the robustness of the registration. Registration with a median error of 1.47 mm was achieved. Read more

Quantitative Analysis of Cardiac Tissue Including Fibroblasts Using Three-Dimensional Confocal Microscopy and Image Reconstruction: Towards a Basis for Electrophysiological Modeling

Electrophysiological modeling of cardiac tissue is commonly based on functional and structural properties measured in experiments. Our knowledge of these properties is incomplete, in particular their remodeling in disease. Here, we introduce a methodology for quantitative tissue characterization based on fluorescent labeling, 3-D scanning confocal microscopy, image processing and reconstruction of tissue micro-structure at sub-micrometer resolution. We applied this methodology to normal rabbit ventricular tissue and tissue from hearts with myocardial infarction. Our analysis revealed that the volume fraction of fibroblasts increased from $4.83pm 0.42%$ (mean $pm$ standard deviation) in normal tissue up to $6.51pm 0.38%$ in myocardium from infarcted hearts. The myocyte volume fraction decreased from $76.20pm 9.89%$ in normal to $73.48pm 8.02%$ adjacent to the infarct. Numerical field calculations on 3-D reconstructions of the extracellular space yielded an extracellular longitudinal conductivity of $0.264pm 0.082~{rm S/m}$ with an anisotropy ratio of $2.095pm 1.11$ in normal tissue. Adjacent to the infarct, the longitudinal conductivity increased up to $0.400pm 0.051~{rm S/m}$, but the anisotropy ratio decreased to $1.295pm 0.09$. Our study indicates an increased density of gap junctions proximal to both fibroblasts and myocytes in infarcted versus normal tissue, s- pporting previous hypotheses of electrical coupling of fibroblasts and myocytes in infarcted hearts. We suggest that the presented methodology provides an important contribution to modeling normal and diseased tissue. Applications of the methodology include the clinical characterization of disease-associated remodeling. Read more

A General Framework for Context-Specific Image Segmentation Using Reinforcement Learning

This paper presents an online reinforcement learning framework for medical image segmentation. The concept of context-specific segmentation is introduced such that the model is adaptive not only to a defined objective function but also to the user's intention and prior knowledge. Based on this concept, a general segmentation framework using reinforcement learning is proposed, which can assimilate specific user intention and behavior seamlessly in the background. The method is able to establish an implicit model for a large state-action space and generalizable to different image contents or segmentation requirements based on learning in situ. In order to demonstrate the practical value of the method, example applications of the technique to four different segmentation problems are presented. Detailed validation results have shown that the proposed framework is able to significantly reduce user interaction, while maintaining both segmentation accuracy and consistency. Read more

Model-Driven Harmonic Parameterization of the Cortical Surface: HIP-HOP

In the context of inter subject brain surface matching, we present a parameterization of the cortical surface constrained by a model of cortical organization. The parameterization is defined via an harmonic mapping of each hemisphere surface to a rectangular planar domain that integrates a representation of the model. As opposed to previous landmark-based registration methods we do not match folds between individuals but instead optimize the fit between cortical sulci and specific iso-coordinate axis in the model. This strategy overcomes some limitation to sulcus-based registration techniques such as topological variability in sulcal landmarks across subjects. Experiments on 62 subjects with manually traced sulci are presented and compared with the result of the Freesurfer software. The evaluation involves a measure of dispersion of sulci with both angular and area distortions. We show that the model-based strategy can lead to a natural, efficient and very fast (less than 5 min per hemisphere) method for defining inter subjects correspondences. We discuss how this approach also reduces the problems inherent to anatomically defined landmarks and open the way to the investigation of cortical organization through the notion of orientation and alignment of structures across the cortex. Read more

2013 IEEE NSS/MIC/RTSD

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers. Read more

Application of Radial Ray Based Segmentation to Cervical Lymph Nodes in CT Images

The 3D-segmentation of lymph nodes in computed tomography images is required for staging and disease progression monitoring. Major challenges are shape and size variance, as well as low contrast, image noise, and pathologies. In this paper, radial ray based segmentation is applied to lymph nodes. From a seed point, rays are cast into all directions and an optimization technique determines a radius for each ray based on image appearance and shape knowledge. Lymph node specific appearance cost functions are introduced and their optimal parameters are determined. For the first time, the resulting segmentation accuracy of different appearance cost functions and optimization strategies is compared. Further contributions are extensions to reduce the dependency on the seed point, to support a larger variety of shapes, and to enable interaction. The best results are obtained using graph-cut on a combination of the direction weighted image gradient and accumulated intensities outside a predefined intensity range. Evaluation on 100 lymph nodes shows that with an average symmetric surface distance of 0.41 mm the segmentation accuracy is close to manual segmentation and outperforms existing radial ray and model based methods. The method's inter-observer-variability of 5.9% for volume assessment is lower than the 15.9% obtained using manual segmentation. Read more

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