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Medical Imaging, IEEE Transactions on

Issue 7 • Date July 2009

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Displaying Results 1 - 21 of 21
  • Table of contents

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

    Page(s): C2
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  • Three-Dimensional Microwave Breast Imaging: Dispersive Dielectric Properties Estimation Using Patient-Specific Basis Functions

    Page(s): 969 - 981
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1259 KB) |  | HTML iconHTML  

    Breast imaging via microwave tomography involves estimating the distribution of dielectric properties within the patient's breast on a discrete mesh. The number of unknowns in the discrete mesh can be very large for 3-D imaging, and this results in computational challenges. We propose a new approach where the discrete mesh is replaced with a relatively small number of smooth basis functions. The dimension of the tomography problem is reduced by estimating the coefficients of the basis functions instead of the dielectric properties at each element in the discrete mesh. The basis functions are constructed using knowledge of the location of the breast surface. The number of functions used in the basis can be varied to balance resolution and computational complexity. The reduced dimension of the inverse problem enables application of a computationally efficient, multiple-frequency inverse scattering algorithm in 3-D. The efficacy of the proposed approach is verified using two 3-D anatomically realistic numerical breast phantoms. It is shown for the case of single-frequency microwave tomography that the imaging accuracy is comparable to that obtained when the original discrete mesh is used, despite the reduction of the dimension of the inverse problem. Results are also shown for a multiple-frequency algorithm where it is computationally challenging to use the original discrete mesh. View full abstract»

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  • Optimized Reconstruction Algorithm for Helical CT With Fractional Pitch Between 1PI and 3PI

    Page(s): 982 - 990
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1248 KB) |  | HTML iconHTML  

    We propose an approximate approach to use redundant data outside the 1PI window within the exact Katsevich reconstruction framework. The proposed algorithm allows a flexible selection of the helical pitch, which is useful for clinical applications. Our idea is an extension of the one proposed by Kohler, Bontus, and Koken (2006). It is based on optimizing the contribution weights of convolution families used in exact Katsevich 3PI algorithms, so that the total weight of each Radon plane is as close to 1 as possible. Optimization is based on solving a least squares problem subject to linear constrains. Numerical evaluation shows good noise and artifact reduction properties of the proposed algorithm. View full abstract»

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  • Learning a Channelized Observer for Image Quality Assessment

    Page(s): 991 - 999
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1861 KB) |  | HTML iconHTML  

    It is now widely accepted that image quality should be evaluated using task-based criteria, such as human-observer performance in a lesion-detection task. The channelized Hotelling observer (CHO) has been widely used as a surrogate for human observers in evaluating lesion detectability. In this paper, we propose that the problem of developing a numerical observer can be viewed as a system-identification or supervised-learning problem, in which the goal is to identify the unknown system of the human observer. Following this approach, we explore the possibility of replacing the Hotelling detector within the CHO with an algorithm that learns the relationship between measured channel features and human observer scores. Specifically, we develop a channelized support vector machine (CSVM) which we compare to the CHO in terms of its ability to predict human-observer performance. In the examples studied, we find that the CSVM is better able to generalize to unseen images than the CHO, and therefore may represent a useful improvement on the CHO methodology, while retaining its essential features. View full abstract»

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  • Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans

    Page(s): 1000 - 1010
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (626 KB) |  | HTML iconHTML  

    A novel atlas-based segmentation approach based on the combination of multiple registrations is presented. Multiple atlases are registered to a target image. To obtain a segmentation of the target, labels of the atlas images are propagated to it. The propagated labels are combined by spatially varying decision fusion weights. These weights are derived from local assessment of the registration success. Furthermore, an atlas selection procedure is proposed that is equivalent to sequential forward selection from statistical pattern recognition theory. The proposed method is compared to three existing atlas-based segmentation approaches, namely (1) single atlas-based segmentation, (2) average-shape atlas-based segmentation, and (3) multi-atlas-based segmentation with averaging as decision fusion. These methods were tested on the segmentation of the heart and the aorta in computed tomography scans of the thorax. The results show that the proposed method outperforms other methods and yields results very close to those of an independent human observer. Moreover, the additional atlas selection step led to a faster segmentation at a comparable performance. View full abstract»

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  • Directional View Interpolation for Compensation of Sparse Angular Sampling in Cone-Beam CT

    Page(s): 1011 - 1022
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    In flat detector cone-beam computed tomography and related applications, sparse angular sampling frequently leads to characteristic streak artifacts. To overcome this problem, it has been suggested to generate additional views by means of interpolation. The practicality of this approach is investigated in combination with a dedicated method for angular interpolation of 3-D sinogram data. For this purpose, a novel dedicated shape-driven directional interpolation algorithm based on a structure tensor approach is developed. Quantitative evaluation shows that this method clearly outperforms conventional scene-based interpolation schemes. Furthermore, the image quality trade-offs associated with the use of interpolated intermediate views are systematically evaluated for simulated and clinical cone-beam computed tomography data sets of the human head. It is found that utilization of directionally interpolated views significantly reduces streak artifacts and noise, at the expense of small introduced image blur. View full abstract»

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  • A DTI-Derived Measure of Cortico-Cortical Connectivity

    Page(s): 1023 - 1036
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1451 KB) |  | HTML iconHTML  

    We arm researchers with a simple method to chart a macroscopic cortico-cortical connectivity network in living human subjects. The researcher provides a diffusion-magnetic resonance imaging (MRI) data set and N cortical regions of interest. In return, we provide an N times N structural adjacency matrix (SAM) quantifying the relative connectivity between all cortical region pairs. We also return a connectivity map for each pair to enable visualization of interconnecting fiber bundles. The measure of connectivity we devise is: 1) free of length bias, 2) proportional to fiber bundle cross-sectional area, and 3) invariant to an exchange of seed and target. We construct a 3-D lattice scaffolding (graph) for white-matter by drawing a link between each pair of voxels in a 26-voxel neighborhood for which their two respective principal eigenvectors form a sufficiently small angle. The connectivity between a cortical region pair is then measured as the maximum number of link-disjoint paths that can be established between them in the white-matter graph. We devise an efficient Edmonds-Karp-like algorithm to compute a conservative bound on the maximum number of link-disjoint paths. Using both simulated and authentic diffusion-tensor imaging data, we demonstrate that the number of link-disjoint paths as a measure of connectivity satisfies properties 1)-3), unlike the fraction of intersecting streamlines-the measure intrinsic to most existing probabilistic tracking algorithms. Finally, we present connectivity maps of some notoriously difficult to track longitudinal and contralateral fasciculi. View full abstract»

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  • Automatic Classification for Pathological Prostate Images Based on Fractal Analysis

    Page(s): 1037 - 1050
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (930 KB) |  | HTML iconHTML  

    Accurate grading for prostatic carcinoma in pathological images is important to prognosis and treatment planning. Since human grading is always time-consuming and subjective, this paper presents a computer-aided system to automatically grade pathological images according to Gleason grading system which is the most widespread method for histological grading of prostate tissues. We proposed two feature extraction methods based on fractal dimension to analyze variations of intensity and texture complexity in regions of interest. Each image can be classified into an appropriate grade by using Bayesian, k-NN, and support vector machine (SVM) classifiers, respectively. Leave-one-out and k-fold cross-validation procedures were used to estimate the correct classification rates (CCR). Experimental results show that 91.2%, 93.7%, and 93.7% CCR can be achieved by Bayesian, k-NN, and SVM classifiers, respectively, for a set of 205 pathological prostate images. If our fractal-based feature set is optimized by the sequential floating forward selection method, the CCR can be promoted up to 94.6%, 94.2%, and 94.6%, respectively, using each of the above three classifiers. Experimental results also show that our feature set is better than the feature sets extracted from multiwavelets, Gabor filters, and gray-level co-occurrence matrix methods because it has a much smaller size and still keeps the most powerful discriminating capability in grading prostate images. View full abstract»

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  • Minimally Redundant 2-D Array Designs for 3-D Medical Ultrasound Imaging

    Page(s): 1051 - 1061
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3430 KB) |  | HTML iconHTML  

    In real-time ultrasonic 3-D imaging, in addition to difficulties in fabricating and interconnecting 2-D transducer arrays with hundreds of elements, there are also challenges in acquiring and processing data from a large number of ultrasound channels. The coarray (spatial convolution of the transmit and receive arrays) can be used to find efficient array designs that capture all of the spatial frequency content (a transmit-receive element combination corresponds to a spatial frequency) with a reduced number of active channels and firing events. Eliminating the redundancies in the transmit-receive element combinations and firing events reduces the overall system complexity and improves the frame rate. Here we explore four reduced redundancy 2-D array configurations for miniature 3-D ultrasonic imaging systems. Our approach is based on 1) coarray design with reduced redundancy using different subsets of linear arrays constituting the 2-D transducer array, and 2) 3-D scanning using fan-beams (narrow in one dimension and broad in the other dimension) generated by the transmit linear arrays. We form the overall array response through coherent summation of the individual responses of each transmit-receive array pairs. We present theoretical and simulated point spread functions of the array configurations along with quantitative comparison in terms of the front-end complexity and image quality. View full abstract»

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  • Symmetry-Based Scalable Lossless Compression of 3D Medical Image Data

    Page(s): 1062 - 1072
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2905 KB) |  | HTML iconHTML  

    We propose a novel symmetry-based technique for scalable lossless compression of 3D medical image data. The proposed method employs the 2D integer wavelet transform to decorrelate the data and an intraband prediction method to reduce the energy of the sub-bands by exploiting the anatomical symmetries typically present in structural medical images. A modified version of the embedded block coder with optimized truncation (EBCOT), tailored according to the characteristics of the data, encodes the residual data generated after prediction to provide resolution and quality scalability. Performance evaluations on a wide range of real 3D medical images show an average improvement of 15% in lossless compression ratios when compared to other state-of-the art lossless compression methods that also provide resolution and quality scalability including 3D-JPEG2000, JPEG2000, and H.264/AVC intra-coding. View full abstract»

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  • An Implementation of CalderÓn's Method for 3-D Limited-View EIT

    Page(s): 1073 - 1082
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (756 KB) |  | HTML iconHTML  

    Mathematical interest in electrical impedance tomography has been strong since the publication of Calderon's foundational paper. This paper introduced the idea of applying external voltage patterns to a medium such that, assuming that the medium is sufficiently close to a constant admittivity, the reconstruction can be accomplished directly by inverse Fourier transform. Motivated by Calderon's method, we have developed a variant of the algorithm which is applicable to the case of measurement on only a part of the boundary and on discrete electrodes. Here we determine voltage or current patterns to apply to the electrodes which optimally approximate Calderon's special functions in the interior. Furthermore, in three dimensions and higher, Calderon's method allows each point in Fourier space to be computed in a multiplicity of ways. We show that by making use of the inherent redundancy in our measurements, we can significantly improve the quality of the static images produced by our algorithm. View full abstract»

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  • Multislice Radio-Frequency Current Density Imaging

    Page(s): 1083 - 1092
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (795 KB) |  | HTML iconHTML  

    Radio-frequency current density imaging (RF-CDI) is an imaging technique that noninvasively measures current density distribution at the Larmor frequency utilizing magnetic resonance imaging (MRI). Previously implemented RF-CDI techniques were only able to image a single slice transverse to the static magnetic field B0 . This paper describes the first realization of a multislice RF-CDI sequence on a 1.5 T clinical imager. Multislice RF current density images have been reconstructed for two phantoms. The influence of MRI random noise on the sensitivity of the multislice RF-CDI measurement has also been studied by theoretical analysis, simulation and phantom experiments. View full abstract»

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  • Improved Time Series Reconstruction for Dynamic Magnetic Resonance Imaging

    Page(s): 1093 - 1104
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3281 KB) |  | HTML iconHTML  

    Time series of in vivo magnetic resonance images exhibit high levels of temporal correlation. Higher temporal resolution reconstructions are obtained by acquiring data at a fraction of the Nyquist rate and resolving the resulting aliasing using the correlation information. The dynamic imaging experiment is modeled as a linear dynamical system. A Kalman filter based unaliasing reconstruction is described for accelerated dynamic magnetic resonance imaging (MRI). The algorithm handles arbitrary readout trajectories naturally. The reconstruction is causal and very fast, making it applicable to real-time imaging. In vivo results are presented for cardiac MRI of healthy volunteers. View full abstract»

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  • Morphodynamic Analysis of Cerebral Aneurysm Pulsation From Time-Resolved Rotational Angiography

    Page(s): 1105 - 1116
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1832 KB) |  | HTML iconHTML  

    This paper presents a technique to estimate and model patient-specific pulsatility of cerebral aneurysms over one cardiac cycle, using 3D rotational X-ray angiography (3DRA) acquisitions. Aneurysm pulsation is modeled as a time varying B-spline tensor field representing the deformation applied to a reference volume image, thus producing the instantaneous morphology at each time point in the cardiac cycle. The estimated deformation is obtained by matching multiple simulated projections of the deforming volume to their corresponding original projections. A weighting scheme is introduced to account for the relevance of each original projection for the selected time point. The wide coverage of the projections, together with the weighting scheme, ensures motion consistency in all directions. The technique has been tested on digital and physical phantoms that are realistic and clinically relevant in terms of geometry, pulsation and imaging conditions. Results from digital phantom experiments demonstrate that the proposed technique is able to recover subvoxel pulsation with an error lower than 10% of the maximum pulsation in most cases. The experiments with the physical phantom allowed demonstrating the feasibility of pulsation estimation as well as identifying different pulsation regions under clinical conditions. View full abstract»

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  • Treatment of Rabbit Elastase-Induced Aneurysm Models by Flow Diverters: Development of Quantifiable Indexes of Device Performance Using Digital Subtraction Angiography

    Page(s): 1117 - 1125
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    It has been known for more than a decade that intracranial aneurysms can be successfully treated by deploying a porous meshed tube in the parent vessel of the aneurysm. Such devices are currently called flow diverters because they promote intraneurysmal flow stasis and thrombosis by diverting blood flow away from the aneurysm sac. The objective of this study was to use angiographic data to quantify and compare the performance of flow diverters of original design in successfully occluding an experimental aneurysm model. Three different configurations of a novel flow diverter with varying porosities and pore densities were implanted in 30 rabbit elastase-induced aneurysms. Temporal variations in angiographic contrast intensity within the aneurysms were fit to a mathematical model. Optimized model parameters were supplemented by the angiographic percentage aneurysm occlusion and an angiographic measure of device flexibility to derive composite scores of performance. Angiographic quantification further suggested a parameter, which could be employed to estimate long-term aneurysm occlusion probabilities immediately after treatment. Performance scores showed that the device with a porosity of 70% and pore density of 18 pores/mm2 performed better than devices with 65% porosity, 14 pores/mm2, and 70% porosity, 12 pores/mm2 with relative efficacies of 100%, 84%, and 76%, respectively. The pore density of flow diverters, rather than porosity, may thus be a critical factor modulating device efficacy. A value of the prognostic parameter of less than 30 predicted greater than 97% angiographic aneurysm occlusion over six months with a sensitivity of 73% and specificity of 82%. View full abstract»

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  • Vulnerable Atherosclerotic Plaque Elasticity Reconstruction Based on a Segmentation-Driven Optimization Procedure Using Strain Measurements: Theoretical Framework

    Page(s): 1126 - 1137
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1250 KB) |  | HTML iconHTML  

    It is now recognized that prediction of the vulnerable coronary plaque rupture requires not only an accurate quantification of fibrous cap thickness and necrotic core morphology but also a precise knowledge of the mechanical properties of plaque components. Indeed, such knowledge would allow a precise evaluation of the peak cap-stress amplitude, which is known to be a good biomechanical predictor of plaque rupture. Several studies have been performed to reconstruct a Young's modulus map from strain elastograms. It seems that the main issue for improving such methods does not rely on the optimization algorithm itself, but rather on preconditioning requiring the best estimation of the plaque components' contours. The present theoretical study was therefore designed to develop: (1) a preconditioning model to extract the plaque morphology in order to initiate the optimization process, and (2) an approach combining a dynamic segmentation method with an optimization procedure to highlight the modulogram of the atherosclerotic plaque. This methodology, based on the continuum mechanics theory prescribing the strain field, was successfully applied to seven intravascular ultrasound coronary lesion morphologies. The reconstructed cap thickness, necrotic core area, calcium area, and the Young's moduli of the calcium, necrotic core, and fibrosis were obtained with mean relative errors of 12%, 4% and 1%, 43%, 32%, and 2%, respectively. View full abstract»

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  • Special issue on multivariate microscopy image analysis

    Page(s): 1138
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  • IEEE copyright form

    Page(s): 1139 - 1140
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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.

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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