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Medical Imaging, IEEE Transactions on

Issue 4 • Date April 2009

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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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    Freely Available from IEEE
  • Pulse Wave Imaging of Normal and Aneurysmal Abdominal Aortas In Vivo

    Page(s): 477 - 486
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1055 KB) |  | HTML iconHTML  

    The abdominal aortic aneurysm (AAA) is a common vascular disease. The current clinical criterion for treating AAAs is an increased diameter above a critical value. However, the maximum diameter does not correlate well with aortic rupture, the main cause of death from AAA disease. AAA disease leads to changes in the aortic wall mechanical properties. The pulse-wave velocity (PWV) may indicate such a change. Because of limitations in temporal and spatial resolution, the widely used foot-to-foot method measures the global, instead of regional, PWV between two points at a certain distance in the circulation. However, mechanical properties are nonuniform along the normal and pathological (e.g., the AAA and atherosclerosis) arteries; thus, such changes are typically regional. Pulse-wave imaging (PWI) has been developed by our group to map the pulse-wave propagation along the abdominal aorta in mice in vivo. By using a retrospective electrocardiogram (ECG) gating technique, the radio-frequency (RF) signals over one cardiac cycle were obtained in murine aortas at the extremely high frame rate of 8 kHz and with a field-of-view (FOV) of 12 times 12 mm2. The velocities of the aortic wall were estimated using an RF-based speckle tracking method. An Angiotensin II (AngII) infusion-based AAA model was used to simulate the human AAA case. Sequences of wall velocity images can noninvasively and quantitatively map the propagation of the pulse wave along the aortic wall. In the normal and sham aortas, the propagation of the pulse wave was relatively uniform along the wall, while in the AngII-treated aortas, the propagation was shown to be nonuniform. There was no significant difference (p > 0.05) in the PWV between sham (4.67 plusmn 1.15 m/s, n = 5) and AngII-treated (4.34 plusmn 1.48 m/s, n = 17) aortas. The correlation coefficient of the linear regression was significantly higher (p < 0.005) in the sham aortas (0.89 plusmn 0.03, n = 5) than in the - ngII-treated ones (0.61 plusmn 0.15, n = 17). The wall velocities induced by the pulse wave were lower and the pulse wave moved nonuniformly along the AngII-treated aorta (p < 0.005), with the lowest velocities at the aneurysmal regions. The discrepancy in the regional wall velocity and the nonuniform pulse-wave propagation along the AngII-treated aorta indicated the inhomogeneities in the aortic wall properties, and the reduced wall velocities indicated stiffening of the aneurysmal wall. This novel technique may thus constitute an early detection tool of vascular degeneration as well as serve as a suitable predictor of AAA rupture, complementary to the current clinical screening practice. View full abstract»

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  • A Representation and Classification Scheme for Tree-Like Structures in Medical Images: Analyzing the Branching Pattern of Ductal Trees in X-ray Galactograms

    Page(s): 487 - 493
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1060 KB) |  | HTML iconHTML  

    We propose a multistep approach for representing and classifying tree-like structures in medical images. Tree-like structures are frequently encountered in biomedical contexts; examples are the bronchial system, the vascular topology, and the breast ductal network. We use tree encoding techniques, such as the depth-first string encoding and the Prufer encoding, to obtain a symbolic string representation of the tree's branching topology; the problem of classifying trees is then reduced to string classification. We use the tf-idf text mining technique to assign a weight of significance to each string term (i.e., tree node label). Similarity searches and k-nearest neighbor classification of the trees is performed using the tf-idf weight vectors and the cosine similarity metric. We applied our approach to characterize the ductal tree-like parenchymal structure in X-ray galactograms, in order to distinguish among different radiological findings. Experimental results demonstrate the effectiveness of the proposed approach with classification accuracy reaching up to 86%, and also indicate that our method can potentially aid in providing insight to the relationship between branching patterns and function or pathology. View full abstract»

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  • A Knowledge-Based Approach to Soft Tissue Reconstruction of the Cervical Spine

    Page(s): 494 - 507
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5208 KB) |  | HTML iconHTML  

    For surgical planning in spine surgery, the segmentation of anatomical structures is a prerequisite. Past efforts focussed on the segmentation of vertebrae from tomographic data, but soft tissue structures have, for the most part, been neglected. Only sparse research work has been done for the spinal cord and the trachea. However, as far as the author is aware, there is no work on segmenting intervertebral discs. Therefore, a totally automatic reconstruction algorithm for the most relevant cervical structures is presented. It is implemented as a straightforward process, using anatomical knowledge which is, in concept, transferrable to other tissues of the human body. No seed points are required since the discs, as initial landmarks, are located via an object recognition approach. The spinal musculature is reconstructed by surface analysis on already segmented vertebrae, thus it can be taken into account in a biomechanical simulation. The segmentation results of our approach showed 91% accordance with expert segmentations and the computation time is less than 1 min on a standard PC. Since the presented system follows some general concepts this approach may also be considered as a step towards full body segmentation of the human. View full abstract»

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  • Combined Volumetric and Surface Registration

    Page(s): 508 - 522
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2549 KB) |  | HTML iconHTML  

    In this paper, we propose a novel method for the registration of volumetric images of the brain that optimizes the alignment of both cortical and subcortical structures. In order to achieve this, relevant geometrical information is extracted from a surface-based morph and diffused into the volume using the Navier operator of elasticity, resulting in a volumetric warp that aligns cortical folding patterns. This warp field is then refined with an intensity driven optical flow procedure that registers noncortical regions, while preserving the cortical alignment. The result is a combined surface and volume morph (CVS) that accurately registers both cortical and subcortical regions, establishing a single coordinate system suitable for the entire brain. View full abstract»

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  • Experimental Comparison of Lesion Detectability for Four Fully-3D PET Reconstruction Schemes

    Page(s): 523 - 534
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1979 KB) |  | HTML iconHTML  

    The objective of this work was to evaluate the lesion detection performance of four fully-3D positron emission tomography (PET) reconstruction schemes using experimentally acquired data. A multi-compartment anthropomorphic phantom was set up to mimic whole-body 18F-fluorodeoxyglucose (FDG) cancer imaging and scanned 12 times in 3D mode, obtaining count levels typical of noisy clinical scans. Eight of the scans had 26 68Ge ldquoshell-lessrdquo lesions (6, 8-, 10-, 12-, 16-mm diameter) placed throughout the phantom with various target:background ratios. This provided lesion-present and lesion-absent datasets with known truth appropriate for evaluating lesion detectability by localization receiver operating characteristic (LROC) methods. Four reconstruction schemes were studied: 1) Fourier rebinning (FORE) followed by 2D attenuation-weighted ordered-subsets expectation-maximization, 2) fully-3D AW-OSEM, 3) fully-3D ordinary-Poisson line-of-response (LOR-)OSEM; and 4) fully-3D LOR-OSEM with an accurate point-spread function (PSF) model. Two forms of LROC analysis were performed. First, a channelized nonprewhitened (CNPW) observer was used to optimize processing parameters (number of iterations, post-reconstruction filter) for the human observer study. Human observers then rated each image and selected the most-likely lesion location. The area under the LROC curve ( A LROC) and the probability of correct localization were used as figures-of-merit. The results of the human observer study found no statistically significant difference between FORE and AW-OSEM3D ( A LROC=0.41 and 0.36, respectively), an increase in lesion detection performance for LOR-OSEM3D ( A LROC=0.45, p=0.076), and additional improvement with the use of the PSF model ( A LROC=0.55, p=0.024). The numerical CNPW observer provided the same rankings among algorithms, but obtained different values of - i>A LROC. These results show improved lesion detection performance for the reconstruction algorithms with more sophisticated statistical and imaging models as compared to the previous-generation algorithms. View full abstract»

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  • Using the Model-Based Residual Bootstrap to Quantify Uncertainty in Fiber Orientations From Q -Ball Analysis

    Page(s): 535 - 550
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3213 KB) |  | HTML iconHTML  

    Bootstrapping of repeated diffusion-weighted image datasets enables nonparametric quantification of the uncertainty in the inferred fiber orientation. The wild bootstrap and the residual bootstrap are model-based residual resampling methods which use a single dataset. Previously, the wild bootstrap method has been presented as an alternative to conventional bootstrapping for diffusion tensor imaging. Here we present a study of an implementation of model-based residual bootstrapping using q -ball analysis and compare the outputs with conventional bootstrapping. We show that model-based residual bootstrap q-ball generates results that closely match the output of the conventional bootstrap. Both the residual and conventional bootstrap of multifiber methods can be used to estimate the probability of different numbers of fiber populations existing in different brain tissues. Also, we have shown that these methods can be used to provide input for probabilistic tractography, avoiding existing limitations associated with data calibration and model selection. View full abstract»

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  • B_1 Homogenization in MRI by Multilayer Coupled Coils

    Page(s): 551 - 554
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (535 KB) |  | HTML iconHTML  

    Transmit B 1 + field homogenization in high-field ( > 3.0 T) human magnetic resonance imaging (MRI) is challenging due to radio-frequency wavelength effects. An approach based on appropriately coupling surface coils to a volume coil was investigated. Electromagnetic simulation results demonstrated the feasibility and effectiveness of this method in proton MRI of the human head at 7.0 T. View full abstract»

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  • Topology-graph Directed Separating Boundary Surfaces Approximation of Nonmanifold Neuroanatomical Structures: Application to Mouse Brain Olfactory Bulb

    Page(s): 555 - 563
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (936 KB) |  | HTML iconHTML  

    Boundary surface approximation of 3-D neuroanatomical regions from sparse 2-D images (e.g., mouse brain olfactory bulb structures from a 2-D brain atlas) has proven to be difficult due to the presence of abutting, shared boundary surfaces that are not handled by traditional boundary-representation data structures and surfaces-from-contours algorithms. We describe a data structure and an algorithm to reconstruct separating surfaces among multiple regions from sparse cross-sectional contours. We define a topology graph for each region, that describes the topological skeleton of the region's boundary surface and that shows between which contours the surface patches should be generated. We provide a graph-directed triangulation algorithm to reconstruct surface patches between contours. We combine our graph-directed triangulation algorithm together with a piecewise parametric curve fitting technique to ensure that abutting or shared surface patches are precisely coincident. We show that our method overcomes limitations in 1) traditional contours-from-surfaces algorithms that assume binary, not multiple, regionalization of space, and in 2) few existing separating surfaces algorithms that assume conversion of input into a regular volumetric grid, which is not possible with sparse interplanar resolution. View full abstract»

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  • Low-Frequency Magnetic Subsurface Imaging: Reconstructing Conductivity Images of Biological Tissues via Magnetic Measurements

    Page(s): 564 - 570
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (626 KB) |  | HTML iconHTML  

    A new data acquisition system has been developed. This system measures the external magnetic fields due to induced currents in the body at a relatively low operation frequency of 50 kHz . Data is obtained by scanning a 2-D area on the body surface. For each transmitter position, a single sample (averaged) of the field distribution is used for image reconstruction. The Steepest Descent Algorithm is used to solve the inverse problem related to the field profiles. High-resolution images of agar blocks and an anesthetized leech are presented. The system sensitivity is measured as 13.2 mV/(S/m) using saline solution phantoms and as 155 V/S using resistors. The signal to noise ratio in the measurements is calculated to be 35.44 dB. The linearity in the measurements is explored using saline solutions in the biological conductivity range. The nonlinearity is measured to be 3.96% of the full scale. The nonlinearity is found to be 0.12% when resistor phantoms are used. The spatial resolution in the conductivity images is measured as 9.36 mm for a 7.5-mm-diameter cylindrical agar object. The results show that it is possible to distinguish two bars separated 14.4 mm from each other. View full abstract»

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  • Oriented Active Shape Models

    Page(s): 571 - 584
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1700 KB) |  | HTML iconHTML  

    Active shape models (ASM) are widely employed for recognizing anatomic structures and for delineating them in medical images. In this paper, a novel strategy called oriented active shape models (OASM) is presented in an attempt to overcome the following five limitations of ASM: 1) lower delineation accuracy, 2) the requirement of a large number of landmarks, 3) sensitivity to search range, 4) sensitivity to initialization, and 5) inability to fully exploit the specific information present in the given image to be segmented. OASM effectively combines the rich statistical shape information embodied in ASM with the boundary orientedness property and the globally optimal delineation capability of the live wire methodology of boundary segmentation. The latter characteristics allow live wire to effectively separate an object boundary from other nonobject boundaries with similar properties especially when they come very close in the image domain. The approach leads to a two-level dynamic programming method, wherein the first level corresponds to boundary recognition and the second level corresponds to boundary delineation, and to an effective automatic initialization method. The method outputs a globally optimal boundary that agrees with the shape model if the recognition step is successful in bringing the model close to the boundary in the image. Extensive evaluation experiments have been conducted by utilizing 40 image (magnetic resonance and computed tomography) data sets in each of five different application areas for segmenting breast, liver, bones of the foot, and cervical vertebrae of the spine. Comparisons are made between OASM and ASM based on precision, accuracy, and efficiency of segmentation. Accuracy is assessed using both region-based false positive and false negative measures and boundary-based distance measures. The results indicate the following: 1) The accuracy of segmentation via OASM is considerably better than that of ASM; 2) The number of landmarks c- n be reduced by a factor of 3 in OASM over that in ASM; 3) OASM becomes largely independent of search range and initialization becomes automatic. All three benefits of OASM ensue mainly from the severe constraints brought in by the boundary-orientedness property of live wire and the globally optimal solution found by the 2-level dynamic programming algorithm. View full abstract»

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  • The Application of Compressed Sensing for Photo-Acoustic Tomography

    Page(s): 585 - 594
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4713 KB) |  | HTML iconHTML  

    Photo-acoustic (PA) imaging has been developed for different purposes, but recently, the modality has gained interest with applications to small animal imaging. As a technique it is sensitive to endogenous optical contrast present in tissues and, contrary to diffuse optical imaging, it promises to bring high resolution imaging for in vivo studies at midrange depths (3-10 mm). Because of the limited amount of radiation tissues can be exposed to, existing reconstruction algorithms for circular tomography require a great number of measurements and averaging, implying long acquisition times. Time-resolved PA imaging is therefore possible only at the cost of complex and expensive electronics. This paper suggests a new reconstruction strategy using the compressed sensing formalism which states that a small number of linear projections of a compressible image contain enough information for reconstruction. By directly sampling the image to recover in a sparse representation, it is possible to dramatically reduce the number of measurements needed for a given quality of reconstruction. View full abstract»

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  • Automated Detection of Regional Wall Motion Abnormalities Based on a Statistical Model Applied to Multislice Short-Axis Cardiac MR Images

    Page(s): 595 - 607
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1358 KB) |  | HTML iconHTML  

    In this paper, a statistical shape analysis method for myocardial contraction is presented that was built to detect and locate regional wall motion abnormalities (RWMA). For each slice level (base, middle, and apex), 44 short-axis magnetic resonance images were selected from healthy volunteers to train a statistical model of normal myocardial contraction using independent component analysis (ICA). A classification algorithm was constructed from the ICA components to automatically detect and localize abnormally contracting regions of the myocardium. The algorithm was validated on 45 patients suffering from ischemic heart disease. Two validations were performed; one with visual wall motion scores (VWMS) and the other with wall thickening (WT) used as references. Accuracy of the ICA-based method on each slice level was 69.93% (base), 89.63% (middle), and 72.78% (apex) when WT was used as reference, and 63.70% (base), 67.41% (middle), and 66.67% (apex) when VWMS was used as reference. From this we conclude that the proposed method is a promising diagnostic support tool to assist clinicians in reducing the subjectivity in VWMS. View full abstract»

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  • Analysis of Penalized Likelihood Image Reconstruction for Dynamic PET Quantification

    Page(s): 608 - 620
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1649 KB) |  | HTML iconHTML  

    Quantification of tracer kinetics using dynamic positron emission tomography (PET) provides important information for understanding the physiological and biochemical processes in humans and animals. A common procedure is to reconstruct a sequence of dynamic images first, and then apply kinetic analysis to the time activity curve of a region of interest derived from the reconstructed images. Obviously, the choice of image reconstruction method and its parameters affect the accuracy of the time activity curve and hence the estimated kinetic parameters. This paper analyzes the effects of penalized likelihood image reconstruction on tracer kinetic parameter estimation. Approximate theoretical expressions are derived to study the bias, variance, and ensemble mean squared error of the estimated kinetic parameters. Computer simulations show that these formulae predict correctly the changes of these statistics as functions of the regularization parameter. It is found that the choice of the regularization parameter has a significant impact on kinetic parameter estimation, indicating proper selection of image reconstruction parameters is important for dynamic PET. A practical method has been developed to use the theoretical formulae to guide the selection of the regularization parameter in dynamic PET image reconstruction. View full abstract»

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  • Automatic Segmentation of Pulmonary Segments From Volumetric Chest CT Scans

    Page(s): 621 - 630
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (814 KB) |  | HTML iconHTML  

    Automated extraction of pulmonary anatomy provides a foundation for computerized analysis of computed tomography (CT) scans of the chest. A completely automatic method is presented to segment the lungs, lobes and pulmonary segments from volumetric CT chest scans. The method starts with lung segmentation based on region growing and standard image processing techniques. Next, the pulmonary fissures are extracted by a supervised filter. Subsequently the lung lobes are obtained by voxel classification where the position of voxels in the lung and relative to the fissures are used as features. Finally, each lobe is subdivided in its pulmonary segments by applying another voxel classification that employs features based on the detected fissures and the relative position of voxels in the lobe. The method was evaluated on 100 low-dose CT scans obtained from a lung cancer screening trial and compared to estimates of both interobserver and intraobserver agreement. The method was able to segment the pulmonary segments with high accuracy (77%), comparable to both interobserver and intraobserver accuracy (74% and 80%, respectively). View full abstract»

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  • Erratum to “An EM Approach to MAP Solution of Segmenting Tissue Mixtures: A Numerical Analysis”

    Page(s): 631
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    The author affiliations in the first footnote of the above-named work are corrected. View full abstract»

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  • Join the IEEE Engineering in Medicine and Biology Society [advertisement]

    Page(s): 632
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    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging Information for authors

    Page(s): C3
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    Freely Available from IEEE
  • 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