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Medical Imaging, IEEE Transactions on

Issue 3 • Date March 2011

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Displaying Results 1 - 25 of 32
  • Table of contents

    Page(s): C1 - C4
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    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging publication information

    Page(s): C2
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    Freely Available from IEEE
  • PET Image Reconstruction Using Information Theoretic Anatomical Priors

    Page(s): 537 - 549
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1384 KB) |  | HTML iconHTML  

    We describe a nonparametric framework for incorporating information from co-registered anatomical images into positron emission tomographic (PET) image reconstruction through priors based on information theoretic similarity measures. We compare and evaluate the use of mutual information (MI) and joint entropy (JE) between feature vectors extracted from the anatomical and PET images as priors in PET reconstruction. Scale-space theory provides a framework for the analysis of images at different levels of detail, and we use this approach to define feature vectors that emphasize prominent boundaries in the anatomical and functional images, and attach less importance to detail and noise that is less likely to be correlated in the two images. Through simulations that model the best case scenario of perfect agreement between the anatomical and functional images, and a more realistic situation with a real magnetic resonance image and a PET phantom that has partial volumes and a smooth variation of intensities, we evaluate the performance of MI and JE based priors in comparison to a Gaussian quadratic prior, which does not use any anatomical information. We also apply this method to clinical brain scan data using Fallypride, a tracer that binds to dopamine receptors and therefore localizes mainly in the striatum. We present an efficient method of computing these priors and their derivatives based on fast Fourier transforms that reduce the complexity of their convolution-like expressions. Our results indicate that while sensitive to initialization and choice of hyperparameters, information theoretic priors can reconstruct images with higher contrast and superior quantitation than quadratic priors. View full abstract»

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  • In Vivo Mapping of Brain Elasticity in Small Animals Using Shear Wave Imaging

    Page(s): 550 - 558
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (950 KB) |  | HTML iconHTML  

    A combination of radiation force and ultrafast ultra-sound imaging is used to both generate and track the propagation of a shear wave in the brain whose local speed is directly related to stiffness, characterized by the dynamic shear modulus G*. When performed on trepanated rats, this approach called shear wave imaging (SWI) provides 3-D brain elasticity maps reaching a spatial resolution of 0.7 mm × 1 mm × 0.4 mm with a good reproducibility (<;13%). The dynamic shear modulus of brain tissues exhibits values in the 2-25 kPa range with a mean value of 12 kPa and is quantified for different anatomical regions. The anisotropy of the shear wave propagation is studied and the first in vivo anisotropy map of brain elasticity is provided. The propagation is found to be isotropic in three gray matter regions but highly anisotropic in two white matter regions. The good temporal resolution (~10 ms per acquisition) of SWI also allows a dynamic estimation of brain elasticity to within a single cardiac cycle, showing that brain pulsatility does not transiently modify local elasticity. SWI proves its potential for the study of pathological modifications of brain elasticity both in small animal models and in clinical intra-operative imaging. View full abstract»

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  • Mosaic Decomposition: An Electronic Cleansing Method for Inhomogeneously Tagged Regions in Noncathartic CT Colonography

    Page(s): 559 - 574
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4825 KB) |  | HTML iconHTML  

    Electronic cleansing (EC) is a method that segments fecal material tagged by an X-ray-opaque oral contrast agent in computed tomographic colonography (CTC) images, and effectively removes the material for digitally cleansing the colon. In this study, we developed a novel EC method, called mosaic decomposition (MD), for reduction of the artifacts due to incomplete cleansing of inhomogeneously tagged fecal material in CTC images, especially in noncathartic CTC images. In our approach, the entire colonic region, including the residual fecal regions, was first decomposed into a set of local homogeneous regions, called tiles, after application of a 3-D watershed transform to the CTC images. Each tile was then subjected to a single-class support vector machine (SVM) classifier for soft-tissue discrimination. The feature set of the soft-tissue SVM classifier was selected by a genetic algorithm (GA). A scalar index, called a soft-tissue likelihood, is formulated for differentiation of the soft-tissue tiles from those of other materials. Then, EC based on MD, called MD-cleansing, is performed by first initializing of the level-set front with the classified tagged regions; the front is then evolved by use of a speed function that was designed, based on the soft-tissue index, to reserve the submerged soft-tissue structures while suppressing the residual fecal regions. The performance of the MD-cleansing method was evaluated by use of a phantom and of clinical cases. In the phantom evaluation, our MD-cleansing was trained with the supine (prone) scan and tested on the prone (supine) scan, respectively. In both cases, the sensitivity and specificity of classification were 100%. The average cleansing ratio was 90.6%, and the soft-tissue preservation ratio was 97.6%. In the clinical evaluation, 10 noncathartic CTC cases (20 scans) were collected, and the ground truth of a total of 2095 tiles was established by manual assignment of a material class to each tile. Five cases were ra- domly selected for training GA/SVM, and the remaining five cases were used for testing. The overall sensitivity and specificity of the proposed classification scheme were 97.1% and 85.3%, respectively, and the accuracy was 94.6%. The area under the ROC curve (Az) was 0.96. Our results indicated that the use of MD-cleansing substantially improved the effectiveness of our EC method in the reduction of incomplete cleansing artifacts. View full abstract»

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  • Fast MR Image Reconstruction for Partially Parallel Imaging With Arbitrary k -Space Trajectories

    Page(s): 575 - 585
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2687 KB) |  | HTML iconHTML  

    Both acquisition and reconstruction speed are crucial for magnetic resonance (MR) imaging in clinical applications. In this paper, we present a fast reconstruction algorithm for SENSE in partially parallel MR imaging with arbitrary k-space trajectories. The proposed method is a combination of variable splitting, the classical penalty technique and the optimal gradient method. Variable splitting and the penalty technique reformulate the SENSE model with sparsity regularization as an unconstrained minimization problem, which can be solved by alternating two simple minimizations: One is the total variation and wavelet based denoising that can be quickly solved by several recent numerical methods, whereas the other one involves a linear inversion which is solved by the optimal first order gradient method in our algorithm to significantly improve the performance. Comparisons with several recent parallel imaging algorithms indicate that the proposed method significantly improves the computation efficiency and achieves state-of-the-art reconstruction quality. View full abstract»

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  • Automated Mitosis Detection of Stem Cell Populations in Phase-Contrast Microscopy Images

    Page(s): 586 - 596
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1327 KB) |  | HTML iconHTML  

    Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations. View full abstract»

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  • Tissue Characterization of Equine Tendons With Clinical B-Scan Images Using a Shock Filter Thinning Algorithm

    Page(s): 597 - 605
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2721 KB) |  | HTML iconHTML  

    The fiber bundle density (FBD) calculated from ultrasound B-scan images of the equine superficial digital flexor tendon (SDFT) can serve as an objective measurement to characterize the three metacarpal sites of normal SDFTs, and also to discriminate a healthy SDFT from an injured one. In this paper, we propose a shock filter algorithm for the thinning of hyper-echoic structures observed in B-scan images of the SDFT. This algorithm is further enhanced by applying closing morphological operations on filtered images to facilitate extraction and quantification of fiber bundle fascicles. The mean FBD values were calculated from a clinical B-scan image dataset of eight normal and five injured SDFTs. The FBD values measured at three different tendon sites in normal cases show a highest density on the proximal site (five cases out of eight) and a lowest value on the distal part (seven cases out of eight). The mean FBD values measured on the entire tendon from the whole B-scan image dataset show a significant difference between normal and injured SDFTs: 51 for the normal SDFTs and 39 for the injured ones . This difference likely indicates disruption of some fiber fascicle bundles where lesions occurred. To conclude, the potential of this imaging technique is shown to be efficient for anatomical structural SDFT characterizations, and opens the way to clinically identifying the integrity of SDFTs. View full abstract»

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  • Algorithm-Enabled Low-Dose Micro-CT Imaging

    Page(s): 606 - 620
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8859 KB) |  | HTML iconHTML  

    Micro-computed tomography (micro-CT) is an important tool in biomedical research and preclinical applications that can provide visual inspection of and quantitative information about imaged small animals and biological samples such as vasculature specimens. Currently, micro-CT imaging uses projection data acquired at a large number (300-1000) of views, which can limit system throughput and potentially degrade image quality due to radiation-induced deformation or damage to the small animal or specimen. In this work, we have investigated low-dose micro-CT and its application to specimen imaging from substantially reduced projection data by using a recently developed algorithm, referred to as the adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) algorithm, which reconstructs an image through minimizing the image total-variation and enforcing data constraints. To validate and evaluate the performance of the ASD-POCS algorithm, we carried out quantitative evaluation studies in a number of tasks of practical interest in imaging of specimens of real animal organs. The results show that the ASD-POCS algorithm can yield images with quality comparable to that obtained with existing algorithms, while using one-sixth to one quarter of the 361-view data currently used in typical micro-CT specimen imaging. View full abstract»

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  • An Optimal Transportation Approach for Nuclear Structure-Based Pathology

    Page(s): 621 - 631
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (657 KB) |  | HTML iconHTML  

    Nuclear morphology and structure as visualized from histopathology microscopy images can yield important diagnostic clues in some benign and malignant tissue lesions. Precise quantitative information about nuclear structure and morphology, however, is currently not available for many diagnostic challenges. This is due, in part, to the lack of methods to quantify these differences from image data. We describe a method to characterize and contrast the distribution of nuclear structure in different tissue classes (normal, benign, cancer, etc.). The approach is based on quantifying chromatin morphology in different groups of cells using the optimal transportation (Kantorovich-Wasserstein) metric in combination with the Fisher discriminant analysis and multidimensional scaling techniques. We show that the optimal transportation metric is able to measure relevant biological information as it enables automatic determination of the class (e.g., normal versus cancer) of a set of nuclei. We show that the classification accuracies obtained using this metric are, on average, as good or better than those obtained utilizing a set of previously described numerical features. We apply our methods to two diagnostic challenges for surgical pathology: one in the liver and one in the thyroid. Results automatically computed using this technique show potentially biologically relevant differences in nuclear structure in liver and thyroid cancers. View full abstract»

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  • A Global Spatial Similarity Optimization Scheme to Track Large Numbers of Dendritic Spines in Time-Lapse Confocal Microscopy

    Page(s): 632 - 641
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1284 KB) |  | HTML iconHTML  

    Dendritic spines form postsynaptic contact sites in the central nervous system. The rapid and spontaneous morphology changes of spines have been widely observed by neurobiologists. Determining the relationship between dendritic spine morphology change and its functional properties such as memory learning is a fundamental yet challenging problem in neurobiology research. In this paper, we propose a novel algorithm to track the morphology change of multiple spines simultaneously in time-lapse neuronal images based on nonrigid registration and integer programming. We also propose a robust scheme to link disappearing-and-reappearing spines. Performance comparisons with other state-of-the-art cell and spine tracking algorithms, and the ground truth show that our approach is more accurate and robust, and it is capable of tracking a large number of neuronal spines in time-lapse confocal microscopy images. View full abstract»

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  • Minimization of Imaging Gradient Effects in Diffusion Tensor Imaging

    Page(s): 642 - 654
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1168 KB) |  | HTML iconHTML  

    A new, sample independent optimization criterion for minimizing the effect of the imaging gradients, including the directional awareness they create, is defined for diffusion tensor imaging (DTI) experiments. The DTI linear algebraic framework is expanded to a normed space to design optimal diffusion gradient schemes (DGS) in an integral fashion without separating the magnitude and direction of the DGS vectors. The feasible space of DGS vectors, for which the estimation equations are determinate, thus a hard constraint for the optimization, is parametrized. Newly generated optimal DGSs demonstrate on an isotropic sample and an ex-vivo baboon brain that the optimization goals are reached as well as a significant increase in estimation performance. View full abstract»

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  • Rigid Body Motion Compensation for Spiral Projection Imaging

    Page(s): 655 - 665
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4323 KB) |  | HTML iconHTML  

    Spiral projection imaging (SPI) is a 3D, spiral based magnetic resonance imaging (MRI) acquisition scheme that allows for self-navigated motion estimation of all six degrees-of-freedom. The trajectory, a set of spiral planes, is enhanced to accommodate motion tracking by adding orthogonal planes. Rigid-body motion tracking is accomplished by comparing the overlapping data and deducing the motion that is consistent with the comparisons. The accuracy of the proposed method is quantified for simulated data and for data collected using both a phantom and a volunteer. These tests were repeated to measure the effect of off-resonance blurring, coil sensitivity, gradient warping, undersampling, and nonrigid motion (e.g., neck). The artifacts of off-resonance, coils sensitivity, and gradient warping impose an unnotable effect on the accuracy of motion estimation. The worst mean accuracy is 0.15 and 0.20 mm for the phantom while the worst mean accuracy is 0.48 and 0.34 mm when imaging a brain, indicating that the nonrigid component in human subjects slightly degrades accuracy. When applied to in vivo motion, the proposed technique considerably reduces motion artifact. View full abstract»

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  • Shear Wave Velocity Imaging Using Transient Electrode Perturbation: Phantom and ex vivo Validation

    Page(s): 666 - 678
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1111 KB) |  | HTML iconHTML  

    This paper presents a new shear wave velocity imaging technique to monitor radio-frequency and microwave ablation procedures, coined electrode vibration elastography. A piezoelectric actuator attached to an ablation needle is transiently vibrated to generate shear waves that are tracked at high frame rates. The time-to-peak algorithm is used to reconstruct the shear wave velocity and thereby the shear modulus variations. The feasibility of electrode vibration elastography is demonstrated using finite element models and ultrasound simulations, tissue-mimicking phantoms simulating fully (phantom 1) and partially ablated (phantom 2) regions, and an ex vivo bovine liver ablation experiment. In phantom experiments, good boundary delineation was observed. Shear wave velocity estimates were within 7% of mechanical measurements in phantom 1 and within 17% in phantom 2. Good boundary delineation was also demonstrated in the ex vivo experiment. The shear wave velocity estimates inside the ablated region were higher than mechanical testing estimates, but estimates in the untreated tissue were within 20% of mechanical measurements. A comparison of electrode vibration elastography and electrode displacement elastography showed the complementary information that they can provide. Electrode vibration elastography shows promise as an imaging modality that provides ablation boundary delineation and quantitative information during ablation procedures. View full abstract»

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  • General Approach to First-Order Error Prediction in Rigid Point Registration

    Page(s): 679 - 693
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (758 KB) |  | HTML iconHTML  

    A general approach to the first-order analysis of error in rigid point registration is presented that accommodates fiducial localization error (FLE) that may be inhomogeneous (varying from point to point) and anisotropic (varying with direction) and also accommodates arbitrary weighting that may also be inhomogeneous and anisotropic. Covariances are derived for target registration error (TRE) and for weighted fiducial registration error (FRE) in terms of covariances of FLE, culminating in a simple implementation that encompasses all combinations of weightings and anisotropy. Furthermore, it is shown that for ideal weighting, in which the weighting matrix for each fiducial equals the inverse of the square root of the cross covariance of its two-space FLE, fluctuations of FRE and TRE are mutually independent. These results are validated by comparison with previously published expressions and by simulation. Furthermore, simulations for randomly generated fiducial positions and FLEs are presented that show that correlation is negligible in the exact case for both ideal and uniform weighting (i.e., no weighting), the latter of which is employed in commercial surgical guidance systems. From these results we conclude that for these weighting schemes, while valid expressions exist relating the covariance of FRE to the covariance of TRE, there are no measures of the goodness of fit of the fiducials for a given registration that give to first order any information about the fluctuation of TRE from its expected value and none that give useful information in the exact case. Therefore, as estimators of registration accuracy, such measures should be approached with extreme caution both by the purveyors of guidance systems and by the practitioners who use them. View full abstract»

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  • Parallel MR Image Reconstruction Using Augmented Lagrangian Methods

    Page(s): 694 - 706
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    Magnetic resonance image (MRI) reconstruction using SENSitivity Encoding (SENSE) requires regularization to suppress noise and aliasing effects. Edge-preserving and sparsity-based regularization criteria can improve image quality, but they demand computation-intensive nonlinear optimization. In this paper, we present novel methods for regularized MRI reconstruction from undersampled sensitivity encoded data-SENSE-reconstruction-using the augmented Lagrangian (AL) framework for solving large-scale constrained optimization problems. We first formulate regularized SENSE-reconstruction as an unconstrained optimization task and then convert it to a set of (equivalent) constrained problems using variable splitting. We then attack these constrained versions in an AL framework using an alternating minimization method, leading to algorithms that can be implemented easily. The proposed methods are applicable to a general class of regularizers that includes popular edge-preserving (e.g., total-variation) and sparsity-promoting (e.g., -norm of wavelet coefficients) criteria and combinations thereof. Numerical experiments with synthetic and in vivo human data illustrate that the proposed AL algorithms converge faster than both general-purpose optimization algorithms such as nonlinear conjugate gradient (NCG) and state-of-the-art MFISTA. View full abstract»

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  • Multichannel Image Registration by Feature-Based Information Fusion

    Page(s): 707 - 720
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4404 KB) |  | HTML iconHTML  

    This paper proposes a novel nonrigid inter-subject multichannel image registration method which combines information from different modalities/channels to produce a unified joint registration. Multichannel images are created using co-registered multimodality images of the same subject to utilize information across modalities comprehensively. Contrary to the existing methods which combine the information at the image/intensity level, the proposed method uses feature-level information fusion method to spatio-adaptively combine the complementary information from different modalities that characterize different tissue types, through Gabor wavelets transformation and Independent Component Analysis (ICA), to produce a robust inter-subject registration. Experiments on both simulated and real multichannel images illustrate the applicability and robustness of the proposed registration method that combines information across modalities. This inter-subject registration is expected to pave the way for subsequent unified population-based multichannel studies. View full abstract»

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  • Graph Run-Length Matrices for Histopathological Image Segmentation

    Page(s): 721 - 732
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2453 KB) |  | HTML iconHTML  

    The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from “graph run-length matrices” lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation. View full abstract»

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  • X-ray Categorization and Retrieval on the Organ and Pathology Level, Using Patch-Based Visual Words

    Page(s): 733 - 746
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3969 KB) |  | HTML iconHTML  

    In this study we present an efficient image categorization and retrieval system applied to medical image databases, in particular large radiograph archives. The methodology is based on local patch representation of the image content, using a “bag of visual words” approach. We explore the effects of various parameters on system performance, and show best results using dense sampling of simple features with spatial content, and a nonlinear kernel-based support vector machine (SVM) classifier. In a recent international competition the system was ranked first in discriminating orientation and body regions in X-ray images. In addition to organ-level discrimination, we show an application to pathology-level categorization of chest X-ray data, the most popular examination in radiology. The system discriminates between healthy and pathological cases, and is also shown to successfully identify specific pathologies in a set of chest radiographs taken from a routine hospital examination. This is a first step towards similarity-based categorization, which has a major clinical implications for computer-assisted diagnostics. View full abstract»

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  • Diffeomorphic Image Registration of Diffusion MRI Using Spherical Harmonics

    Page(s): 747 - 758
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1595 KB) |  | HTML iconHTML  

    Nonrigid registration of diffusion magnetic resonance imaging (MRI) is crucial for group analyses and building white matter and fiber tract atlases. Most current diffusion MRI registration techniques are limited to the alignment of diffusion tensor imaging (DTI) data. We propose a novel diffeomorphic registration method for high angular resolution diffusion images by mapping their orientation distribution functions (ODFs). ODFs can be reconstructed using q-ball imaging (QBI) techniques and represented by spherical harmonics (SHs) to resolve intra-voxel fiber crossings. The registration is based on optimizing a diffeomorphic demons cost function. Unlike scalar images, deforming ODF maps requires ODF reorientation to maintain its consistency with the local fiber orientations. Our method simultaneously reorients the ODFs by computing a Wigner rotation matrix at each voxel, and applies it to the SH coefficients during registration. Rotation of the coefficients avoids the estimation of principal directions, which has no analytical solution and is time consuming. The proposed method was validated on both simulated and real data sets with various metrics, which include the distance between the estimated and simulated transformation fields, the standard deviation of the general fractional anisotropy and the directional consistency of the deformed and reference images. The registration performance using SHs with different maximum orders were compared using these metrics. Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps. View full abstract»

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  • Novel Scatter Compensation of List-Mode PET Data Using Spatial and Energy Dependent Corrections

    Page(s): 759 - 773
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4374 KB) |  | HTML iconHTML  

    With the widespread use of positron emission tomography (PET) crystals with greatly improved energy resolution (e.g., 11.5% with LYSO as compared to 20% with BGO) and of list-mode acquisitions, the use of the energy of individual events in scatter correction schemes becomes feasible. We propose a novel scatter approach that incorporates the energy of individual photons in the scatter correction and reconstruction of list-mode PET data in addition to the spatial information presently used in clinical scanners. First, we rewrite the Poisson likelihood function of list-mode PET data including the energy distributions of primary and scatter co incidences and show that this expression yields an MLEM reconstruction algorithm containing both energy and spatial dependent corrections. To estimate the spatial distribution of scatter coincidences we use the single scatter simulation (SSS). Next, we derive two new formulae which allow estimation of the 2-D (coincidences) energy probability density functions (E-PDF) of primary and scatter coincidences from the 1-D (photons) E-PDFs associated with each photon. We also describe an accurate and robust object-specific method for estimating these 1-D E-PDFs based on a de composition of the total energy spectra detected across the scanner into primary and scattered components. Finally, we show that the energy information can be used to accurately normalize the scatter sinogram to the data. We compared the performance of this novel scatter correction incorporating both the position and energy of detected coincidences to that of the traditional approach modeling only the spatial distribution of scatter coincidences in 3-D Monte Carlo simulations of a medium cylindrical phantom and a large, nonuniform NCAT phantom. Incorporating the energy information in the scatter correction decreased bias in the activity distribution estimation by ~20% and ~40% in the cold regions of the large NCAT phantom at energy resolutions 11.5% and 20% at 511 k- V, respectively, compared to when using the spatial information alone. View full abstract»

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  • Active Volume Models for Medical Image Segmentation

    Page(s): 774 - 791
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3080 KB) |  | HTML iconHTML  

    In this paper, we propose a novel predictive model, active volume model (AVM), for object boundary extraction. It is a dynamic “object” model whose manifestation includes a deformable curve or surface representing a shape, a volumetric interior carrying appearance statistics, and an embedded classifier that separates object from background based on current feature information. The model focuses on an accurate representation of the foreground object's attributes, and does not explicitly represent the background. As we will show, however, the model is capable of reasoning about the background statistics thus can detect when is change sufficient to invoke a boundary decision. When applied to object segmentation, the model alternates between two basic operations: 1) deforming according to current region of interest (ROI), which is a binary mask representing the object region predicted by the current model, and 2) predicting ROI according to current appearance statistics of the model. To further improve robustness and accuracy when segmenting multiple objects or an object with multiple parts, we also propose multiple-surface active volume model (MSAVM), which consists of several single-surface AVM models subject to high-level geometric spatial constraints. An AVM's deformation is derived from a linear system based on finite element method (FEM). To keep the model's surface triangulation optimized, surface remeshing is derived from another linear system based on Laplacian mesh optimization (LMO) , . Thus efficient optimization and fast convergence of the model are achieved by solving two linear systems. Segmentation, validation and comparison results are presented from experiments on a variety of 2-D and 3-D medical images. View full abstract»

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  • Prostate Segmentation in HIFU Therapy

    Page(s): 792 - 803
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3907 KB) |  | HTML iconHTML  

    Prostate segmentation in 3-D transrectal ultrasound images is an important step in the definition of the intra-operative planning of high intensity focused ultrasound (HIFU) therapy. This paper presents two main approaches for the semi-automatic methods based on discrete dynamic contour and optimal surface detection. They operate in 3-D and require a minimal user interaction. They are considered both alone or sequentially combined, with and without postregularization, and applied on anisotropic and isotropic volumes. Their performance, using different metrics, has been evaluated on a set of 28 3-D images by comparison with two expert delineations. For the most efficient algorithm, the symmetric average surface distance was found to be 0.77 mm. View full abstract»

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  • Simultaneous Reconstruction of Activity and Attenuation for PET/MR

    Page(s): 804 - 813
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2564 KB) |  | HTML iconHTML  

    Medical investigations targeting a quantitative analysis of the position emission tomography (PET) images require the incorporation of additional knowledge about the photon attenuation distribution in the patient. Today, energy range adapted attenuation maps derived from computer tomography (CT) scans are used to effectively compensate for image quality degrading effects, such as attenuation and scatter. Replacing CT by magnetic resonance (MR) is considered as the next evolutionary step in the field of hybrid imaging systems. However, unlike CT, MR does not measure the photon attenuation and thus does not provide an easy access to this valuable information. Hence, many research groups currently investigate different technologies for MR-based attenuation correction (MR-AC). Typically, these approaches are based on techniques such as special acquisition sequences (alone or in combination with subsequent image processing), anatomical atlas registration, or pattern recognition techniques using a data base of MR and corresponding CT images. We propose a generic iterative reconstruction approach to simultaneously estimate the local tracer concentration and the attenuation distribution using the segmented MR image as anatomical reference. Instead of applying predefined attenuation values to specific anatomical regions or tissue types, the gamma attenuation at 511 keV is determined from the PET emission data. In particular, our approach uses a maximum-likelihood estimation for the activity and a gradient-ascent based algorithm for the attenuation distribution. The adverse effects of scattered and accidental gamma coincidences on the quantitative accuracy of PET, as well as artifacts caused by the inherent crosstalk between activity and attenuation estimation are efficiently reduced using enhanced decay event localization provided by time-of-flight PET, accurate correction for accidental coincidences, and a reduced number of unknown attenuation coefficients. First results a- hieved with measured whole body PET data and reference segmentation from CT showed an absolute mean difference of 0.005 cm in the lungs, 0.0009 cm in case of fat, and 0.0015 cm for muscles and blood. The proposed method indicates a robust and reliable alternative to other MR-AC approaches targeting patient specific quantitative analysis in time-of-flight PET/MR. View full abstract»

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  • Region Detection by Minimizing Intraclass Variance With Geometric Constraints, Global Optimality, and Efficient Approximation

    Page(s): 814 - 827
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    Efficient segmentation of globally optimal surfaces in volumetric images is a central problem in many medical image analysis applications. Intraclass variance has been successfully utilized for object segmentation, for instance, in the Chan-Vese model, especially for images without prominent edges. In this paper, we study the optimization problem of detecting a region (volume) between two coupled smooth surfaces by minimizing the intraclass variance using an efficient polynomial-time algorithm. Our algorithm is based on the shape probing technique in computational geometry and computes a sequence of minimum-cost closed sets in a derived parametric graph. The method has been validated on computer-synthetic volumetric images and in X-ray CT-scanned datasets of plexiglas tubes of known sizes. Its applicability to clinical data sets was also demonstrated. In all cases, the approach yielded highly accurate results. We believe that the developed technique is of interest on its own. We expect that it can shed some light on solving other important optimization problems arising in medical imaging. Furthermore, we report an approximation algorithm which runs much faster than the exact algorithm while yielding highly comparable segmentation accuracy. View full abstract»

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Aims & Scope

IEEE Transactions on Medical Imaging (T-MI) encourages the submission of manuscripts on imaging of body structures, morphology and function, and imaging of microscopic biological entities. The journal publishes original contributions on medical imaging achieved by various modalities, such as ultrasound, X-rays (including CT) magnetic resonance, radionuclides, microwaves, and light, as well as medical image processing and analysis, visualization, pattern recognition, and related methods. Studies involving highly technical perspectives are most welcome. The journal focuses on a unified common ground where instrumentation, systems, components, hardware and software, mathematics and physics contribute to the studies.

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Meet Our Editors

Editor-in-Chief
Milan Sonka
Iowa Institute for Biomedical Imaging
3016B SC, Department of Electrical and Computer Engineering
The University of Iowa
Iowa City, IA  52242  52242  USA
milan-sonka@uiowa.edu