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Medical Imaging, IEEE Transactions on

Issue 10 • Date Oct. 2009

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Displaying Results 1 - 21 of 21
  • Table of contents

    Publication Year: 2009 , Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging publication information

    Publication Year: 2009 , Page(s): C2
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  • Comparison of Analytic and Algebraic Methods for Motion-Compensated Cone-Beam CT Reconstruction of the Thorax

    Publication Year: 2009 , Page(s): 1513 - 1525
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5940 KB) |  | HTML iconHTML  

    Respiratory motion is a major concern in cone-beam (CB) computed tomography (CT) of the thorax. It causes artifacts such as blur, streaks, and bands, in particular when using slow-rotating scanners mounted on the gantry of linear accelerators. In this paper, we compare two approaches for motion-compensated CBCT reconstruction of the thorax. The first one is analytic; it is heuristically adapted from the method of Feldkamp, Davis, and Kress (FDK). The second one is algebraic: the system of linear equations is generated using a new algorithm for the projection of deformable volumes and solved using the simultaneous algebraic reconstruction technique (SART). For both methods, we propose to estimate the motion on patient data using a previously acquired 4-D CT image. The methods were tested on two digital and one mechanical motion-controlled phantoms and on a patient dataset. Our results indicate that the two methods correct most motion artifacts. However, the analytic method does not fully correct streaks and bands even if the motion is perfectly estimated due to the underlying approximation. In contrast, the algebraic method allows us full correction of respiratory-induced artifacts. View full abstract»

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  • Shear Modulus Decomposition Algorithm in Magnetic Resonance Elastography

    Publication Year: 2009 , Page(s): 1526 - 1533
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1315 KB) |  | HTML iconHTML  

    Magnetic resonance elastography (MRE) is an imaging modality capable of visualizing the elastic properties of an object using magnetic resonance imaging (MRI) measurements of transverse acoustic strain waves induced in the object by a harmonically oscillating mechanical vibration. Various algorithms have been designed to determine the mechanical properties of the object under the assumptions of linear elasticity, isotropic and local homogeneity. One of the challenging problems in MRE is to reduce the noise effects and to maintain contrast in the reconstructed shear modulus images. In this paper, we propose a new algorithm designed to reduce the degree of noise amplification in the reconstructed shear modulus images without the assumption of local homogeneity. Investigating the relation between the measured displacement data and the stress wave vector, the proposed algorithm uses an iterative reconstruction formula based on a decomposition of the stress wave vector. Numerical simulation experiments and real experiments with agarose gel phantoms and human liver data demonstrate that the proposed algorithm is more robust to noise compared to standard inversion algorithms and stably determines the shear modulus. View full abstract»

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  • Estimating Kinetic Parameter Maps From Dynamic Contrast-Enhanced MRI Using Spatial Prior Knowledge

    Publication Year: 2009 , Page(s): 1534 - 1547
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1801 KB) |  | HTML iconHTML  

    Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of kinetic parameters obtained by fitting a pharmacokinetic model to the observed data. Least squares estimates of the highly nonlinear model parameters, however, can exhibit high variance and can be severely biased. As a remedy, we bring to bear spatial prior knowledge by means of a generalized Gaussian Markov random field (GGMRF). By using information from neighboring voxels and computing the maximum a posteriori solution for entire parameter maps at once, both bias and variance of the parameter estimates can be reduced thus leading to smaller root mean square error (RMSE). Since the number of variables gets very big for common image resolutions, sparse solvers have to be employed. To this end, we propose a generalized iterated conditional modes (ICM) algorithm operating on blocks instead of sites which is shown to converge considerably faster than the conventional ICM algorithm. Results on simulated DCE-MR images show a clear reduction of RMSE and variance as well as, in some cases, reduced estimation bias. The mean residual bias (MRB) is reduced on the simulated data as well as for all 37 patients of a prostate DCE-MRI dataset. Using the proposed algorithm, average computation times only increase by a factor of 1.18 (871 ms per voxel) for a Gaussian prior and 1.51 (1.12 s per voxel) for an edge-preserving prior compared to the single voxel approach (740 ms per voxel). View full abstract»

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  • Fast Large-Tip-Angle Multidimensional and Parallel RF Pulse Design in MRI

    Publication Year: 2009 , Page(s): 1548 - 1559
    Cited by:  Papers (13)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (997 KB) |  | HTML iconHTML  

    Large-tip-angle multidimensional radio-frequency (RF) pulse design is a difficult problem, due to the nonlinear response of magnetization to applied RF at large tip-angles. In parallel excitation, multidimensional RF pulse design is further complicated by the possibility for transmit field patterns to change between subjects, requiring pulses to be designed rapidly while a subject lies in the scanner. To accelerate pulse design, we introduce a fast version of the optimal control method for large-tip-angle parallel excitation. The new method is based on a novel approach to analytically linearizing the Bloch equation about a large-tip-angle RF pulse, which results in an approximate linear model for the perturbations created by adding a small-tip-angle pulse to a large-tip-angle pulse. The linear model can be evaluated rapidly using nonuniform fast Fourier transforms, and we apply it iteratively to produce a sequence of pulse updates that improve excitation accuracy. We achieve drastic reductions in design time and memory requirements compared to conventional optimal control, while producing pulses of similar accuracy. The new method can also compensate for nonidealities such as main field inhomogeneties. View full abstract»

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  • Trabecular Bone Analysis in CT and X-Ray Images of the Proximal Femur for the Assessment of Local Bone Quality

    Publication Year: 2009 , Page(s): 1560 - 1575
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2512 KB) |  | HTML iconHTML  

    Currently, conventional X-ray and CT images as well as invasive methods performed during the surgical intervention are used to judge the local quality of a fractured proximal femur. However, these approaches are either dependent on the surgeon's experience or cannot assist diagnostic and planning tasks preoperatively. Therefore, in this work a method for the individual analysis of local bone quality in the proximal femur based on model-based analysis of CT- and X-ray images of femur specimen will be proposed. A combined representation of shape and spatial intensity distribution of an object and different statistical approaches for dimensionality reduction are used to create a statistical appearance model in order to assess the local bone quality in CT and X-ray images. The developed algorithms are tested and evaluated on 28 femur specimen. It will be shown that the tools and algorithms presented herein are highly adequate to automatically and objectively predict bone mineral density values as well as a biomechanical parameter of the bone that can be measured intraoperatively. View full abstract»

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  • Effect of Domain Shape Modeling and Measurement Errors on the 2-D D-Bar Method for EIT

    Publication Year: 2009 , Page(s): 1576 - 1584
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (733 KB) |  | HTML iconHTML  

    The D-bar algorithm based on Nachman's 2-D global uniqueness proof for the inverse conductivity problem (Nachman, 1996) is implemented on a chest-shaped domain. The scattering transform is computed on this chest-shaped domain using trigonometric and adjacent current patterns and the complete electrode model for the forward problem is computed with the finite element method in order to obtain simulated voltage measurements. The robustness and effectiveness of the method is demonstrated on a simulated chest with errors in input currents, output voltages, electrode placement, and domain modeling. View full abstract»

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  • Noise Reduction in Computed Tomography Scans Using 3-D Anisotropic Hybrid Diffusion With Continuous Switch

    Publication Year: 2009 , Page(s): 1585 - 1594
    Cited by:  Papers (8)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1182 KB) |  | HTML iconHTML  

    Noise filtering techniques that maintain image contrast while decreasing image noise have the potential to optimize the quality of computed tomography (CT) images acquired at reduced radiation dose. In this paper, a hybrid diffusion filter with continuous switch (HDCS) is introduced, which exploits the benefits of three-dimensional edge-enhancing diffusion (EED) and coherence-enhancing diffusion (CED). Noise is filtered, while edges, tubular structures, and small spherical structures are preserved. From ten high dose thorax CT scans, acquired at clinical doses, ultra low dose ( 15 mAs ) scans were simulated and used to evaluate and compare HDCS to other diffusion filters, such as regularized Perona-Malik diffusion and EED. Quantitative results show that the HDCS filter outperforms the other filters in restoring the high dose CT scan from the corresponding simulated low dose scan. A qualitative evaluation was performed on filtered real low dose CT thorax scans. An expert observer scored artifacts as well as fine structures and was asked to choose one of three scans (two filtered (blinded), one unfiltered) for three different settings (trachea, lung, and mediastinal). Overall, the HDCS filtered scan was chosen most often. View full abstract»

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  • Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI

    Publication Year: 2009 , Page(s): 1595 - 1605
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (742 KB) |  | HTML iconHTML  

    Automatic extraction of vertebra regions from a spinal magnetic resonance (MR) image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation system, which consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. In order to produce an efficient and effective vertebra detector, a statistical learning approach based on an improved AdaBoost algorithm is proposed. A robust estimation procedure is applied on the detected vertebra locations to fit a spine curve, thus refining the above vertebra detection results. This refinement process involves removing the false detections and recovering the miss-detected vertebrae. Finally, an iterative normalized-cut segmentation algorithm is proposed to segment the precise vertebra regions from the detected vertebra locations. In our implementation, the proposed AdaBoost-based detector is trained from 22 spinal MR volume images. The experimental results show that the proposed vertebra detection and segmentation system can achieve nearly 98% vertebra detection rate and 96% segmentation accuracy on a variety of testing spinal MR images. Our experiments also show the vertebra detection and segmentation accuracies by using the proposed algorithm are superior to those of the previous representative methods. The proposed vertebra detection and segmentation system is proved to be robust and accurate so that it can be used for advanced research and application on spinal MR images. View full abstract»

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  • Reproducible Classification of Infarct Heterogeneity Using Fuzzy Clustering on Multicontrast Delayed Enhancement Magnetic Resonance Images

    Publication Year: 2009 , Page(s): 1606 - 1614
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (616 KB) |  | HTML iconHTML  

    Delayed enhancement MRI (DE-MRI) can be used to identify myocardial infarct (MI). Classification of MI into the infarct core and heterogeneous periphery (called the gray zone) on conventional inversion-recovery gradient echo (IR-GRE) DE-MRI images has been related to inducibility for ventricular tachycardia. However, this classification is sensitive to image noise, depends on the signal intensity characteristics in a remote region of myocardium, and requires manual contours of the endocardial border. Image analysis and fuzzy clustering techniques were developed to analyze images acquired using a multicontrast delayed enhancement (MCDE) sequence in order characterize the infarct zones. The MCDE analysis is automated and uses data fitting of signal intensities acquired at multiple inversion times. In a study of 15 patients with chronic MI, the gray zones derived from IR-GRE and MCDE images were comparable. The variability in the gray zone size associated with random noise and operator input was significantly reduced using the MCDE-based analysis compared to the IR-GRE-based analysis. In summary, the MCDE approach yields a more reproducible measure of the infarct core and gray zones on any given data set. View full abstract»

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  • Noise Estimation in Magnitude MR Datasets

    Publication Year: 2009 , Page(s): 1615 - 1622
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1459 KB) |  | HTML iconHTML  

    Estimating the noise parameter in magnitude magnetic resonance (MR) images is important in a wide range of applications. We propose an automatic noise estimation method that does not rely on a substantial proportion of voxels being from the background. Specifically, we model the magnitude of the observed signal as a mixture of Rice distributions with common noise parameter. The expectation-maximization (EM) algorithm is used to estimate all parameters, including the common noise parameter. The algorithm needs initializing values for which we provide some strategies that work well. The number of components in the mixture model also needs to be estimated en route to noise estimation and we provide a novel approach to doing so. Our methodology performs very well on a range of simulation experiments and physical phantom data. Finally, the methodology is demonstrated on four clinical datasets. View full abstract»

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  • Controlling Familywise Error Rate for Matched Subspace Detection in Dynamic FDG PET

    Publication Year: 2009 , Page(s): 1623 - 1631
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (742 KB) |  | HTML iconHTML  

    Detection of small lesions in fluorodeoxyglucose (FDG) positron emission tomography (PET) is limited by image resolution and low signal to noise ratio. We have previously described a matched subspace detection method that uses the time activity curve to distinguish tumors from background in dynamic FDG PET. Applying this algorithm on a voxel by voxel basis throughout the dynamic image produces a test statistic image or ldquomaprdquo which on thresholding indicates the potential locations of secondary or metastatic tumors. In this paper, we describe a thresholding method that controls familywise error rate (FWER) for the matched subspace detection statistical map. The method involves three steps. First, the PET image is segmented into several homogeneous regions. Then, the statistical map is normalized to a zero mean unit variance Gaussian random field. Finally, the images are thresholded at a fixed FWER by estimating their spatial smoothness and applying a random field theory maximum statistic approach. We evaluate this thresholding method using digital phantoms generated from clinical dynamic images. We also present an application of the proposed approach to clinical PET data from a breast cancer patient with metastatic disease. View full abstract»

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  • Motor Protein Function in Skeletal Muscle—A Multiple Scale Approach to Contractility

    Publication Year: 2009 , Page(s): 1632 - 1642
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1370 KB) |  | HTML iconHTML  

    We present an approach to skeletal muscle contractility and its regulation over different scales ranging from biomechanical studies in intact muscle fibers down to the motility and interaction of single motor protein molecules. At each scale, shortening velocities as a measure for weak cross-bridge cycling rates are extracted and compared. Experimental approaches include transmitted light microscopy, second harmonic generation imaging of contracting myofibrils, and fluorescence microscopy of single molecule motility. Each method yields image sequences that are analyzed with automated image processing algorithms to extract the contraction velocity. Using this approach, we show how to isolate the contribution of the motor proteins actin and myosin and their modulation by regulatory proteins from the concerted action of electro-mechanical activation on a more complex cellular scale. The advantage of this approach is that averaged contraction velocities can be determined on the different scales ranging from isolated motor proteins to sarcomere levels in myofibrils and myofibril arrays within the cellular architecture. Our results show that maximum shortening velocities during in situ electrical activation of sarcomere contraction in intact single muscle cells can substantially deviate from sliding velocities obtained in oriented in vitro motility assays of isolated motor proteins showing that biophysical contraction kinetics not simply translate linearly between contractility scales. To adequately resolve the very fast initial mechanical activation kinetics of shortening at each scale, it was necessary to implement high-speed imaging techniques. In the case of intact fibers and single molecule motility, we achieved a major increase in temporal resolution up to frame rates of 200-1000 fps using CMOS image sensor technology. The data we obtained at this unprecedented temporal resolution and the parameters extracted can be used to validate results obtained f- om computational models of motor protein interaction and skeletal muscle contractility in health and muscle disease. Our approach is feasible to explain the possible underlying mechanisms that contribute to different shortening velocities at different scales and complexities. View full abstract»

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  • Tomographic Reconstruction of Three-Dimensional Volumes Using the Distorted Born Iterative Method

    Publication Year: 2009 , Page(s): 1643 - 1653
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (656 KB) |  | HTML iconHTML  

    Although real imaging problems involve objects that have variations in three dimensions, a majority of work examining inverse scattering methods for ultrasonic tomography considers 2-D imaging problems. Therefore, the study of 3-D inverse scattering methods is necessary for future applications of ultrasonic tomography. In this work, 3-D reconstructions using different arrays of rectangular elements focused on elevation were studied when reconstructing spherical imaging targets by producing a series of 2-D image slices using the 2-D distorted Born iterative method (DBIM). The effects of focal number f/#, speed of sound contrast Deltac, and scatterer size were considered. For comparison, the 3-D wave equation was also inverted using point-like transducers to produce fully 3-D DBIM image reconstructions. In 2-D slicing, blurring in the vertical direction was highly correlated with the transmit/receive elevation point-spread function of the transducers for low Deltac. The eventual appearance of overshoot artifacts in the vertical direction were observed with increasing Deltac. These diffraction-related artifacts were less severe for smaller focal number values and larger spherical target sizes. When using 3-D DBIM, the overshoot artifacts were not observed and spatial resolution was improved. However, results indicate that array configuration in 3-D reconstructions is important for good image reconstruction. Practical arrays were designed and assessed for image reconstruction using 3-D DBIM. View full abstract»

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  • ISBI 2010

    Publication Year: 2009 , Page(s): 1654
    Save to Project icon | Request Permissions | PDF file iconPDF (601 KB)  
    Freely Available from IEEE
  • ISBI 2010

    Publication Year: 2009 , Page(s): 1654
    Save to Project icon | Request Permissions | PDF file iconPDF (601 KB)  
    Freely Available from IEEE
  • Special issue on multivariate microscopy image analysis

    Publication Year: 2009 , Page(s): 1655
    Save to Project icon | Request Permissions | PDF file iconPDF (157 KB)  
    Freely Available from IEEE
  • Have you visited lately? www.ieee.org [advertisement]

    Publication Year: 2009 , Page(s): 1656
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    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging Information for authors

    Publication Year: 2009 , Page(s): C3
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    Freely Available from IEEE
  • Blank page [back cover]

    Publication Year: 2009 , Page(s): C4
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    Freely Available from IEEE

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