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Medical Imaging, IEEE Transactions on

Issue 1 • Date Jan. 2007

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  • Table of contents

    Page(s): C1
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  • IEEE Transactions on Medical Imaging publication information

    Page(s): C2
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  • Volumetric Texture Segmentation by Discriminant Feature Selection and Multiresolution Classification

    Page(s): 1 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6199 KB) |  | HTML iconHTML  

    In this paper, a multiresolution volumetric texture segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via subband filtering using an orientation pyramid (Wilson and Spann, 1988). A novel Bhattacharyya space, based on the Bhattacharyya distance, is proposed for selecting the most discriminant measurements and producing a compact feature space. An oct tree is built of the multivariate features space and a chosen level at a lower spatial resolution is first classified. The classified voxel labels are then projected to lower levels of the tree where a boundary refinement procedure is performed with a three-dimensional (3-D) equivalent of butterfly filters. The algorithm was tested with 3-D artificial data and three magnetic resonance imaging sets of human knees with encouraging results. The regions segmented from the knees correspond to anatomical structures that can be used as a starting point for other measurements such as cartilage extraction View full abstract»

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  • Tracking Myocardial Motion From Cine DENSE Images Using Spatiotemporal Phase Unwrapping and Temporal Fitting

    Page(s): 15 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3085 KB) |  | HTML iconHTML  

    Displacement encoding with stimulated echoes (DENSE) encodes myocardial tissue displacement into the phase of the MR image. Cine DENSE allows for rapid quantification of myocardial displacement at multiple cardiac phases through the majority of the cardiac cycle. For practical sensitivities to motion, relatively high displacement encoding frequencies are used and phase wrapping typically occurs. In order to obtain absolute measures of displacement, a two-dimensional (2-D) quality-guided phase unwrapping algorithm was adapted to unwrap both spatially and temporally. Both a fully automated algorithm and a faster semi-automated algorithm are proposed. A method for computing the 2-D trajectories of discrete points in the myocardium as they move through the cardiac cycle is introduced. The error in individual displacement measurements is reduced by fitting a time series to sequential displacement measurements along each trajectory. This improvement is in turn reflected in strain maps, which are derived directly from the trajectories. These methods were validated both in vivo and on a rotating phantom. Further measurements were made to optimize the displacement encoding frequency and to estimate the baseline strain noise both on the phantom and in vivo. The fully automated phase unwrapping algorithm was successful for 767 out of 800 images (95.9%), and the semi-automated algorithm was successful for 786 out of 800 images (98.3%). The accuracy of the tracking algorithm for typical cardiac displacements on a rotating phantom is 0.24plusmn0.15mm. The optimal displacement encoding frequency is in the region of 0.1 cycles/mm, and, for 2 scans of 17-s duration, the strain noise after temporal fitting was estimated to be 2.5plusmn3.0% at end-diastole, 3.1plusmn3.1% at end-systole, and 5.3plusmn5.0% in mid-diastole. The improvement in intra-myocardial strain measurements due to temporal fitting is apparent in strain histograms, and also in identifying regions of dysfunctional myo- - cardium in studies of patients with infarcts View full abstract»

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  • Full Motion and Flow Field Recovery From Echo Doppler Data

    Page(s): 31 - 45
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    We present a new computational method for reconstructing a vector velocity field from scattered, pulsed-wave ultrasound Doppler data. The main difficulty is that the Doppler measurements are incomplete, for they do only capture the velocity component along the beam direction. We thus propose to combine measurements from different beam directions. However, this is not yet sufficient to make the problem well posed because 1) the angle between the directions is typically small and 2) the data is noisy and nonuniformly sampled. We propose to solve this reconstruction problem in the continuous domain using regularization. The reconstruction is formulated as the minimizer of a cost that is a weighted sum of two terms: 1) the sum of squared difference between the Doppler data and the projected velocities 2) a quadratic regularization functional that imposes some smoothness on the velocity field. We express our solution for this minimization problem in a B-spline basis, obtaining a sparse system of equations that can be solved efficiently. Using synthetic phantom data, we demonstrate the significance of tuning the regularization according to the a priori knowledge about the physical property of the motion. Next, we validate our method using real phantom data for which the ground truth is known. We then present reconstruction results obtained from clinical data that originate from 1) blood flow in carotid bifurcation and 2) cardiac wall motion View full abstract»

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  • Statistical Analyses of Brain Surfaces Using Gaussian Random Fields on 2-D Manifolds

    Page(s): 46 - 57
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (461 KB) |  | HTML iconHTML  

    Interest in the morphometric analysis of the brain and its subregions has recently intensified because growth or degeneration of the brain in health or illness affects not only the volume but also the shape of cortical and subcortical brain regions, and new image processing techniques permit detection of small and highly localized perturbations in shape or localized volume, with remarkable precision. An appropriate statistical representation of the shape of a brain region is essential, however, for detecting, localizing, and interpreting variability in its surface contour and for identifying differences in volume of the underlying tissue that produce that variability across individuals and groups of individuals. Our statistical representation of the shape of a brain region is defined by a reference region for that region and by a Gaussian random field (GRF) that is defined across the entire surface of the region. We first select a reference region from a set of segmented brain images of healthy individuals. The GRF is then estimated as the signed Euclidean distances between points on the surface of the reference region and the corresponding points on the corresponding region in images of brains that have been coregistered to the reference. Correspondences between points on these surfaces are defined through deformations of each region of a brain into the coordinate space of the reference region using the principles of fluid dynamics. The warped, coregistered region of each subject is then unwarped into its native space, simultaneously bringing into that space the map of corresponding points that was established when the surfaces of the subject and reference regions were tightly coregistered. The proposed statistical description of the shape of surface contours makes no assumptions, other than smoothness, about the shape of the region or its GRF. The description also allows for the detection and localization of statistically significant differences in the shapes of t- - he surfaces across groups of subjects at both a fine and coarse scale. We demonstrate the effectiveness of these statistical methods by applying them to study differences in shape of the amygdala and hippocampus in a large sample of normal subjects and in subjects with attention deficit/hyperactivity disorder (ADHD) View full abstract»

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  • A Fast Fully 4-D Incremental Gradient Reconstruction Algorithm for List Mode PET Data

    Page(s): 58 - 67
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (453 KB) |  | HTML iconHTML  

    We describe a fast and globally convergent fully four-dimensional incremental gradient (4DIG) algorithm to estimate the continuous-time tracer density from list mode positron emission tomography (PET) data. Detection of 511-keV photon pairs produced by positron-electron annihilation is modeled as an inhomogeneous Poisson process whose rate function is parameterized using cubic B-splines. The rate functions are estimated by minimizing the cost function formed by the sum of the negative log-likelihood of arrival times, spatial and temporal roughness penalties, and a negativity penalty. We first derive a computable bound for the norm of the optimal temporal basis function coefficients. Based on this bound we then construct and prove convergence of an incremental gradient algorithm. Fully 4-D simulations demonstrate the substantially faster convergence behavior of the 4DIG algorithm relative to preconditioned conjugate gradient. Four-dimensional reconstructions of real data are also included to illustrate the performance of this method View full abstract»

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  • An Optimal Radial Profile Order Based on the Golden Ratio for Time-Resolved MRI

    Page(s): 68 - 76
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2262 KB) |  | HTML iconHTML  

    In dynamic magnetic resonance imaging (MRI) studies, the motion kinetics or the contrast variability are often hard to predict, hampering an appropriate choice of the image update rate or the temporal resolution. A constant azimuthal profile spacing (111.246deg), based on the Golden Ratio, is investigated as optimal for image reconstruction from an arbitrary number of profiles in radial MRI. The profile order is evaluated and compared with a uniform profile distribution in terms of signal-to-noise ratio (SNR) and artifact level. The favorable characteristics of such a profile order are exemplified in two applications on healthy volunteers. First, an advanced sliding window reconstruction scheme is applied to dynamic cardiac imaging, with a reconstruction window that can be flexibly adjusted according to the extent of cardiac motion that is acceptable. Second, a contrast-enhancing k-space filter is presented that permits reconstructing an arbitrary number of images at arbitrary time points from one raw data set. The filter was utilized to depict the T1-relaxation in the brain after a single inversion prepulse. While a uniform profile distribution with a constant angle increment is optimal for a fixed and predetermined number of profiles, a profile distribution based on the Golden Ratio proved to be an appropriate solution for an arbitrary number of profiles View full abstract»

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  • An Optimal Three-Class Linear Observer Derived From Decision Theory

    Page(s): 77 - 83
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (422 KB) |  | HTML iconHTML  

    Many attempts have been made to develop an optimal linear observer for classifying multiclass data. Most approaches either do not have a definite description of optimality or have regions of ambiguity in decision making. In this paper, we derive a three-class Hotelling observer (3-HO), inspired by the ideal observer that results from a decision theoretic solution to the three-class classification problem. Assuming the data vectors follow multivariate Gaussian distributions with equal covariance matrices for the three classes, it is shown that two two-class Hotelling templates construct a 3-HO which has the same performance as the three-class ideal observer (3-IO). We show that, without the Gaussian and equal covariance assumptions, the 3-HO is still applicable when the two-class Hotelling templates of each pair of the classes satisfy a certain linear relationship. In this case, the 3-HO simultaneously maximizes the signal-to-noise (SNR) of the test statistics between each pair of the classes. In conclusion, we developed a three-class linear mathematical observer that uses first- and second-order ensemble data statistics. This mathematical observer, which has clearly defined optimality for several data statistics conditions and has decision rules that have no ambiguous decision regions, is potentially useful in the optimization and evaluation of imaging techniques for performing three-class diagnostic tasks View full abstract»

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  • The Effects of Flow Dispersion and Cardiac Pulsation in Arterial Spin Labeling

    Page(s): 84 - 92
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (879 KB) |  | HTML iconHTML  

    The blood in the carotid arteries exhibits time-varying flow velocity as a function of cardiac phases. Despite this flow velocity variation, most current methods set forth for the analysis of arterial spin labeling (ASL) data have assumed that the tagged blood is delivered from the tagging region to the imaging region via simple plug flow, i.e., a single transit delay (deltat). In this study, we used a pulse oximeter to synchronize image acquisition at systole and diastole separately. The deltat dispersion was modeled with a Gaussian distribution and the effect of cardiac pulsation upon the ASL signal was evaluated on five healthy volunteers. ASL signals were collected at a series of inflow times (TI) using PICORE QUIPSS II: TR/TE/TI1=2400/3.2/700 ms, TI={300,500,700,900,1100,1300,1500} ms, matrix size=64times64,repetition=100. Transit delay was found significantly shorter in systolic tag than diastolic tag (paired student's t-test, p<0.001; mean difference across subjects=54 ms). When the tag was applied in late systole, the ASL signal arrived in the target brain slice earlier, and was higher by 16% with TI=700 ms. Intervoxel dispersion (~350 ms) dominated over intravoxel dispersion (<200 ms). The disparity of ASL signals found between systolic and diastolic tags indicated that ASL imaging was sensitive to cardiac pulsations. We conclude that both flow dispersion and fluctuations in the ASL signal due to cardiac pulsations are significant View full abstract»

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  • COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements

    Page(s): 93 - 105
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1310 KB) |  | HTML iconHTML  

    This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass-preserving template warping method, which results in tissue density maps that constitute local tissue volumetric measurements. Regions that display strong correlations between tissue volume and classification (clinical) variables are extracted using a watershed segmentation algorithm, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy to achieve robustness to outliers. A volume increment algorithm is then applied to these regions to extract regional volumetric features, from which a feature selection technique using support vector machine (SVM)-based criteria is used to select the most discriminative features, according to their effect on the upper bound of the leave-one-out generalization error. Finally, SVM-based classification is applied using the best set of features, and it is tested using a leave-one-out cross-validation strategy. The results on MR brain images of healthy controls and schizophrenia patients demonstrate not only high classification accuracy (91.8% for female subjects and 90.8% for male subjects), but also good stability with respect to the number of features selected and the size of SVM kernel used View full abstract»

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  • Segmenting Articular Cartilage Automatically Using a Voxel Classification Approach

    Page(s): 106 - 115
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1570 KB) |  | HTML iconHTML  

    We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual and automated image analysis techniques, low-field scanners on the other hand produce lower quality images but to a fraction of the cost of their high-field counterpart. For low-field MRI, there is no well-established accuracy validation for quantitative cartilage estimates, but we show that differences between healthy and osteoarthritic populations are statistically significant using our cartilage volume and surface area estimates, which suggests that low-field MRI analysis can become a useful, affordable tool in clinical studies View full abstract»

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  • Segmentation of the Optic Disc, Macula and Vascular Arch in Fundus Photographs

    Page(s): 116 - 127
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2347 KB) |  | HTML iconHTML  

    An automatic system is presented to find the location of the major anatomical structures in color fundus photographs; the optic disc, the macula, and the vascular arch. These structures are found by fitting a single point-distribution-model to the image, that contains points on each structure. The method can handle optic disc and macula centered images of both the left and the right eye. The system uses a cost function, which is based on a combination of both global and local cues, to find the correct position of the model points. The global terms in the cost function are based on the orientation and width of the vascular pattern in the image. The local term is derived from the image structure around the points of the model. To optimize the fit of the point-distribution-model to an image, a sophisticated combination of optimization processes is proposed which combines optimization in the parameter space of the model and in the image space, where points are moved directly. Experimental results are presented demonstrating that our specific choices for the cost function components and optimization scheme are needed to obtain good results. The system was developed and trained on a set of 500 screening images, and tested on a completely independent set of 500 screening images. In addition to this the system was also tested on a separate set of 100 pathological images. In the screening set it was able to find the vascular arch in 93.2%, the macula in 94.4%, the optic disc location in 98.4% and whether it is dealing with a left or right eye in 100% of all tested cases. For the pathological images test set, this was 77.0%, 92.0%, 94.0%, and 100% respectively View full abstract»

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  • Towards a Real-Time Minimally-Invasive Vascular Intervention Simulation System

    Page(s): 128 - 132
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    Recently, foundations rooted in physics have been laid down for the goal of simulating the propagation of a guide wire inside the vasculature. At the heart of the simulation lies the fundamental task of energy minimization. The energy comes from interaction with the vessel wall and the bending of the guide wire. For the simulation to be useful in actual training, obtaining the smallest possible optimization time is key. In this paper, we, therefore, study the influence of using different optimization techniques: a semianalytical approximation algorithm, the conjugate-gradients algorithm, and an evolutionary algorithm (EA), specifically the GLIDE algorithm. Simulation performance has been measured on phantom data. The results show that a substantial reduction in time can be obtained while the error is increased only slightly if conjugate gradients or GLIDE is used View full abstract»

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  • Erratum to “Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures”

    Page(s): 133
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (52 KB) |  | HTML iconHTML  

    In order to detect vessels at a variety of widths, we apply the matched filter at multiple scales (i.e., compute for multiple values and then combine the responses across scales). Unfortunately, the output amplitudes of spatial operators such as derivatives or matched filters generally decrease with increasing scale. To compensate for this effect, Lindeberg introduced gamma-normalized derivatives. We use this notion to define a gamma-normalized matched filter, View full abstract»

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  • IEEE Transactions on Medical Imaging applicants sought for Editor-In-Chief

    Page(s): 134
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  • IEEE Nuclear Science Symposium and Medical Imaging Conference

    Page(s): 135
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  • Order form for reprints

    Page(s): 136
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  • IEEE Transactions on Medical Imaging Information for authors

    Page(s): C3
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  • Blank page [back cover]

    Page(s): C4
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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.

Full Aims & Scope

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