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Medical Imaging, IEEE Transactions on

Issue 10 • Date Oct. 2011

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Displaying Results 1 - 17 of 17
  • Table of contents

    Publication Year: 2011 , Page(s): C1
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  • IEEE Transactions on Medical Imaging publication information

    Publication Year: 2011 , Page(s): C2
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  • Frequency-Domain Optical Tomographic Imaging of Arthritic Finger Joints

    Publication Year: 2011 , Page(s): 1725 - 1736
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2978 KB) |  | HTML iconHTML  

    We are presenting data from the largest clinical trial on optical tomographic imaging of finger joints to date. Overall we evaluated 99 fingers of patients affected by rheumatoid arthritis (RA) and 120 fingers from healthy volunteers. Using frequency-domain imaging techniques we show that sensitivities and specificities of 0.85 and higher can be achieved in detecting RA. This is accomplished by deriving multiple optical parameters from the optical tomographic images and combining them for the statistical analysis. Parameters derived from the scattering coefficient perform slightly better than absorption derived parameters. Furthermore we found that data obtained at 600 MHz leads to better classification results than data obtained at 0 or 300 MHz. View full abstract»

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  • Automatic Nonrigid Calibration of Image Registration for Real Time MR-Guided HIFU Ablations of Mobile Organs

    Publication Year: 2011 , Page(s): 1737 - 1745
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1022 KB) |  | HTML iconHTML  

    Real time magnetic resonance imaging (MRI) is rapidly gaining importance in interventional therapies. An accurate motion estimation is required for mobile targets and can be conveniently addressed using an image registration algorithm. Since the adaptation of the control parameters of the algorithm depends on the application (targeted organ, location of the tumor, slice orientation, etc.), typically an individual calibration is required. However, the assessment of the estimated motion accuracy is difficult since the real target motion is unknown. In this paper, existing criteria based only on anatomical image similarity are demonstrated to be inadequate. A new criterion is introduced, which is based on the local magnetic field distribution. The proposed criterion was used to assess, during a preparative calibration step, the optimal configuration of an image registration algorithm derived from the Horn and Schunck method. The accuracy of the proposed method was evaluated in a moving phantom experiment, which allows the comparison with the known motion pattern and to an established criterion based on anatomical images. The usefulness of the method for the calibration of optical-flow based algorithms was also demonstrated in vivo under conditions similar to thermo-ablation for the abdomen of twelve volunteers. In average over all volunteers, a resulting displacement error of 1.5 mm was obtained (largest observed error equal to 4-5 mm) using a criterion based on anatomical image similarity. A better average accuracy of 1 mm was achieved using the proposed criterion (largest observed error equal to 2 mm). In both kidney and liver, the proposed criterion was shown to provide motion field accuracy in the range of the best achievable. View full abstract»

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  • Simultaneous Multi-scale Registration Using Large Deformation Diffeomorphic Metric Mapping

    Publication Year: 2011 , Page(s): 1746 - 1759
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2532 KB) |  | HTML iconHTML  

    In the framework of large deformation diffeomorphic metric mapping (LDDMM), we present a practical methodology to integrate prior knowledge about the registered shapes in the regularizing metric. Our goal is to perform rich anatomical shape comparisons from volumetric images with the mathematical properties offered by the LDDMM framework. We first present the notion of characteristic scale at which image features are deformed. We then propose a methodology to compare anatomical shape variations in a multi-scale fashion, i.e., at several characteristic scales simultaneously. In this context, we propose a strategy to quantitatively measure the feature differences observed at each characteristic scale separately. After describing our methodology, we illustrate the performance of the method on phantom data. We then compare the ability of our method to segregate a group of subjects having Alzheimer's disease and a group of controls with a classical coarse to fine approach, on standard 3D MR longitudinal brain images. We finally apply the approach to quantify the anatomical development of the human brain from 3D MR longitudinal images of pre-term babies. Results show that our method registers accurately volumetric images containing feature differences at several scales simultaneously with smooth deformations. View full abstract»

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  • Topology-Based Kernels With Application to Inference Problems in Alzheimer's Disease

    Publication Year: 2011 , Page(s): 1760 - 1770
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1608 KB) |  | HTML iconHTML  

    Alzheimer's disease (AD) research has recently witnessed a great deal of activity focused on developing new statistical learning tools for automated inference using imaging data. The workhorse for many of these techniques is the support vector machine (SVM) framework (or more generally kernel-based methods). Most of these require, as a first step, specification of a kernel matrix K between input examples (i.e., images). The inner product between images Ii and Ij in a feature space can generally be written in closed form and so it is convenient to treat K as “given.” However, in certain neuroimaging applications such an assumption becomes problematic. As an example, it is rather challenging to provide a scalar measure of similarity between two instances of highly attributed data such as cortical thickness measures on cortical surfaces. Note that cortical thickness is known to be discriminative for neurological disorders, so leveraging such information in an inference framework, especially within a multi-modal method, is potentially advantageous. But despite being clinically meaningful, relatively few works have successfully exploited this measure for classification or regression. Motivated by these applications, our paper presents novel techniques to compute similarity matrices for such topologically-based attributed data. Our ideas leverage recent developments to characterize signals (e.g., cortical thickness) motivated by the persistence of their topological features, leading to a scheme for simple constructions of kernel matrices. As a proof of principle, on a dataset of 356 subjects from the Alzheimer's Disease Neuroimaging Initiative study, we report good performance on several statistical inference tasks without any feature selection, dimensionality reduction, or parameter tuning. View full abstract»

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  • Magnetic Resonance Electrical Impedance Tomography for Monitoring Electric Field Distribution During Tissue Electroporation

    Publication Year: 2011 , Page(s): 1771 - 1778
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (926 KB) |  | HTML iconHTML  

    Electroporation is a phenomenon caused by externally applied electric field of an adequate strength and duration to cells that results in the increase of cell membrane permeability to various molecules, which otherwise are deprived of transport mechanism. As accurate coverage of the tissue with a sufficiently large electric field presents one of the most important conditions for successful electroporation, applications based on electroporation would greatly benefit with a method of monitoring the electric field, especially if it could be done during the treatment. As the membrane electroporation is a consequence of an induced transmembrane potential which is directly proportional to the local electric field, we propose current density imaging (CDI) and magnetic resonance electrical impedance tomography (MREIT) techniques to measure the electric field distribution during electroporation. The experimental part of the study employs CDI with short high-voltage pulses, while the theoretical part of the study is based on numerical simulations of MREIT. A good agreement between experimental and numerical results was obtained, suggesting that CDI and MREIT can be used to determine the electric field during electric pulse delivery and that both of the methods can be of significant help in planning and monitoring of future electroporation based clinical applications. View full abstract»

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  • Robust Statistical Label Fusion Through Consensus Level, Labeler Accuracy, and Truth Estimation (COLLATE)

    Publication Year: 2011 , Page(s): 1779 - 1794
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2177 KB) |  | HTML iconHTML  

    Segmentation and delineation of structures of interest in medical images is paramount to quantifying and characterizing structural, morphological, and functional correlations with clinically relevant conditions. The established gold standard for performing segmentation has been manual voxel-by-voxel labeling by a neuroanatomist expert. This process can be extremely time consuming, resource intensive and fraught with high inter-observer variability. Hence, studies involving characterizations of novel structures or appearances have been limited in scope (numbers of subjects), scale (extent of regions assessed), and statistical power. Statistical methods to fuse data sets from several different sources (e.g., multiple human observers) have been proposed to simultaneously estimate both rater performance and the ground truth labels. However, with empirical datasets, statistical fusion has been observed to result in visually inconsistent findings. So, despite the ease and elegance of a statistical approach, single observers and/or direct voting are often used in practice. Hence, rater performance is not systematically quantified and exploited during label estimation. To date, statistical fusion methods have relied on characterizations of rater performance that do not intrinsically include spatially varying models of rater performance. Herein, we present a novel, robust statistical label fusion algorithm to estimate and account for spatially varying performance. This algorithm, COnsensus Level, Labeler Accuracy and Truth Estimation (COLLATE), is based on the simple idea that some regions of an image are difficult to label (e.g., confusion regions: boundaries or low contrast areas) while other regions are intrinsically obvious (e.g., consensus regions: centers of large regions or high contrast edges). Unlike its predecessors, COLLATE estimates the consensus level of each voxel and estimates differing models of observer behavior in each region. We show that COLLATE provides- significant improvement in label accuracy and rater assessment over previous fusion methods in both simulated and empirical datasets. View full abstract»

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  • Multi-Pinhole SPECT Calibration: Influence of Data Noise and Systematic Orbit Deviations

    Publication Year: 2011 , Page(s): 1795 - 1807
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3623 KB) |  | HTML iconHTML  

    The geometry of a single pinhole SPECT system with circular orbit can be uniquely determined from a measurement of three point sources, provided that at least two inter-point distances are known. In contrast, it has been shown mathematically that, for a multi-pinhole SPECT system with circular orbit, only two point sources are needed, and the knowledge of the distance between them is not required. In this paper, we report that this conclusion only holds if the motion of the camera is perfectly circular. In reality, the detector heads systematically slightly deviate from the circular orbit, which may introduce non-negligible bias in the estimated parameters and degrade the reconstructed image. An analytical linear model was extended to estimate the influence of both data noise and systematic deviations on the accuracy of the calibration and on the image quality of the reconstruction. It turns out that applying the knowledge of the distances greatly reduces the reconstruction error, especially in the presence of systematic deviations. In addition, we propose that instead of using the information about the distances between the point sources, it is more straightforward to use the knowledge about the distances between the pinhole apertures during multi-pinhole calibration. The two distance-fixing approaches yield similar reconstruction accuracy. Our theoretical results are supported by reconstruction images of a Jaszczak-type phantom scan. View full abstract»

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  • Optimal Rebinning of Time-of-Flight PET Data

    Publication Year: 2011 , Page(s): 1808 - 1818
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2463 KB) |  | HTML iconHTML  

    Time-of-flight (TOF) positron emission tomography (PET) scanners offer the potential for significantly improved signal-to-noise ratio (SNR) and lesion detectability in clinical PET. However, fully 3D TOF PET image reconstruction is a challenging task due to the huge data size. One solution to this problem is to rebin TOF data into a lower dimensional format. We have recently developed Fourier rebinning methods for mapping TOF data into non-TOF formats that retain substantial SNR advantages relative to sinograms acquired without TOF information. However, mappings for rebinning into non-TOF formats are not unique and optimization of rebinning methods has not been widely investigated. In this paper we address the question of optimal rebinning in order to make full use of TOF information. We focus on FORET-3D, which approximately rebins 3D TOF data into 3D non-TOF sinogram formats without requiring a Fourier transform in the axial direction. We optimize the weighting for FORET-3D to minimize the variance, resulting in H2-weighted FORET-3D, which turns out to be the best linear unbiased estimator (BLUE) under reasonable approximations and furthermore the uniformly minimum variance unbiased (UMVU) estimator under Gaussian noise assumptions. This implies that any information loss due to optimal rebinning is as a result only of the approximations used in deriving the rebinning equation and developing the optimal weighting. We demonstrate using simulated and real phantom TOF data that the optimal rebinning method achieves variance reduction and contrast recovery improvement compared to nonoptimized rebinning weightings. In our preliminary study using a simplified simulation setup, the performance of the optimal rebinning method was comparable to that of fully 3D TOF MAP. View full abstract»

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  • A Nonrigid Registration Framework Using Spatially Encoded Mutual Information and Free-Form Deformations

    Publication Year: 2011 , Page(s): 1819 - 1828
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1372 KB) |  | HTML iconHTML  

    Mutual information (MI) registration including spatial information has been shown to perform better than the traditional MI measures for certain nonrigid registration tasks. In this work, we first provide new insight to problems of the MI-based registration and propose to use the spatially encoded mutual information (SEMI) to tackle these problems. To encode spatial information, we propose a hierarchical weighting scheme to differentiate the contribution of sample points to a set of entropy measures, which are associated to spatial variable values. By using free-form deformations (FFDs) as the transformation model, we can first define the spatial variable using the set of FFD control points, and then propose a local ascent optimization scheme for nonrigid SEMI registration. The proposed SEMI registration can improve the registration accuracy in the nonrigid cases where the traditional MI is challenged due to intensity distortion, contrast enhancement, or different imaging modalities. It also has a similar computation complexity to the registration using traditional MI measures, improving up to two orders of magnitude of computation time compared to the traditional schemes. We validate our algorithms using phantom brain MRI, simulated dynamic contrast enhanced mangetic resonance imaging (MRI) of the liver, and in vivo cardiac MRI. The results show that the SEMI registration significantly outperforms the traditional MI registration. View full abstract»

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  • PopTract: Population-Based Tractography

    Publication Year: 2011 , Page(s): 1829 - 1840
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1932 KB) |  | HTML iconHTML  

    White matter fiber tractography plays a key role in the in vivo understanding of brain circuitry. For tract-based comparison of a population of images, a common approach is to first generate an atlas by averaging, after spatial normalization, all images in the population, and then perform tractography using the constructed atlas. The reconstructed fiber trajectories form a common geometry onto which diffusion properties of each individual subject can be projected based on the corresponding locations in the subject native space. However, in the case of high angular resolution diffusion imaging (HARDI), where modeling fiber crossings is an important goal, the above-mentioned averaging method for generating an atlas results in significant error in the estimation of local fiber orientations and causes a major loss of fiber crossings. These limitatitons have significant impact on the accuracy of the reconstructed fiber trajectories and jeopardize subsequent tract-based analysis. As a remedy, we present in this paper a more effective means of performing tractography at a population level. Our method entails determining a bipolar Watson distribution at each voxel location based on information given by all images in the population, giving us not only the local principal orientations of the fiber pathways, but also confidence levels of how reliable these orientations are across subjects. The distribution field is then fed as an input to a probabilistic tractography framework for reconstructing a set of fiber trajectories that are consistent across all images in the population. We observe that the proposed method, called PopTract, results in significantly better preservation of fiber crossings, and hence yields better trajectory reconstruction in the atlas space. View full abstract»

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  • An Anatomically Oriented Breast Coordinate System for Mammogram Analysis

    Publication Year: 2011 , Page(s): 1841 - 1851
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1781 KB) |  | HTML iconHTML  

    We have developed a breast coordinate system that is based on breast anatomy to register female breasts into a common coordinate frame in 2-D mediolateral (ML) or mediolateral oblique (MLO) view mammograms. The breasts are registered according to the location of the pectoral muscle and the nipple and the shape of the breast boundary because these are the most robust features independent of the breast size and shape. On the basis of these landmarks, we have constructed a nonlinear mapping between the parameter frame and the breast region in the mammogram. This mapping makes it possible to identify the corresponding positions and orientations among all of the ML or MLO mammograms, which facilitates an implicit use of the registration, i.e., no explicit image warping is needed. We additionally show how the coordinate transform can be used to extract Gaussian derivative features so that the feature positions and orientations are registered and extracted without nonlinearly deforming the images. We use the proposed breast coordinate transform in a cross-sectional breast cancer risk assessment study of 490 women, in which we attempt to learn breast cancer risk factors from mammograms that were taken prior to when the breast cancer became visible to a radiologist. The coordinate system provides both the relative position and orientation information on the breast region from which the features are derived. In addition, the coordinate system can be used in temporal studies to pinpoint anatomically equivalent locations between the mammograms of each woman and among the mammograms of all of the women in the study. The results of the cross-sectional study show that the classification into cancer and control groups can be improved by using the new coordinate system, compared to other systems evaluated. Comparisons were performed using the area-under-the-receiver-operating-characteristic-curve score. In general, the new coordinate system makes an accurate anatomical registration- of breasts possible, which suggests its wide applicability wherever 2-D mammogram registration is required. View full abstract»

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  • A Supervised Patch-Based Approach for Human Brain Labeling

    Publication Year: 2011 , Page(s): 1852 - 1862
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (946 KB) |  | HTML iconHTML  

    We propose in this work a patch-based image labeling method relying on a label propagation framework. Based on image intensity similarities between the input image and an anatomy textbook, an original strategy which does not require any nonrigid registration is presented. Following recent developments in nonlocal image denoising, the similarity between images is represented by a weighted graph computed from an intensity-based distance between patches. Experiments on simulated and in vivo magnetic resonance images show that the proposed method is very successful in providing automated human brain labeling. View full abstract»

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  • Automatic Aneurysm Neck Detection Using Surface Voronoi Diagrams

    Publication Year: 2011 , Page(s): 1863 - 1876
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2194 KB) |  | HTML iconHTML  

    A new automatic approach for saccular intracranial aneurysm isolation is proposed in this work. Due to the inter- and intra-observer variability in manual delineation of the aneurysm neck, a definition based on a minimum cost path around the aneurysm sac is proposed that copes with this variability and is able to make consistent measurements along different data sets, as well as to automate and speedup the analysis of cerebral aneurysms. The method is based on the computation of a minimal path along a scalar field obtained on the vessel surface, to find the aneurysm neck in a robust and fast manner. The computation of the scalar field on the surface is obtained using a fast marching approach with a speed function based on the exponential of the distance from the centerline bifurcation between the aneurysm dome and the parent vessels. In order to assure a correct topology of the aneurysm sac, the neck computation is constrained to a region defined by a surface Voronoi diagram obtained from the branches of the vessel centerline. We validate this method comparing our results in 26 real cases with manual aneurysm isolation obtained using a cut-plane, and also with results obtained using manual delineations from three different observers by comparing typical morphological measures. View full abstract»

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  • IEEE Transactions on Medical Imaging Information for authors

    Publication Year: 2011 , Page(s): C3
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    Publication Year: 2011 , 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
Michael Insana
Beckman Institute for Advanced Science and Technology
Department of Bioengineering
University of Illinois at Urbana-Champaign
Urbana, IL 61801 USA
m.f.i@ieee.org