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Medical Imaging, IEEE Transactions on

Issue 5 • Date May 2011

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  • 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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  • Guest Editorial Compressive Sensing for Biomedical Imaging

    Publication Year: 2011 , Page(s): 1013 - 1016
    Cited by:  Papers (4)
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  • Signal Compensation and Compressed Sensing for Magnetization-Prepared MR Angiography

    Publication Year: 2011 , Page(s): 1017 - 1027
    Cited by:  Patents (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (2831 KB) |  | HTML iconHTML  

    Magnetization-prepared acquisitions offer a trade-off between image contrast and scan efficiency for magnetic resonance imaging. Because the prepared signals gradually decay, the contrast can be improved by frequently repeating the preparation, which in turn significantly increases the scan time. A common solution is to perform the data collection progressing from low- to high-spatial-frequency sa... View full abstract»

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  • MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning

    Publication Year: 2011 , Page(s): 1028 - 1041
    Cited by:  Papers (51)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (2716 KB) |  | HTML iconHTML  

    Compressed sensing (CS) utilizes the sparsity of magnetic resonance (MR) images to enable accurate reconstruction from undersampled k-space data. Recent CS methods have employed analytical sparsifying transforms such as wavelets, curvelets, and finite differences. In this paper, we propose a novel framework for adaptively learning the sparsifying transform (dictionary), and reconstructing the imag... View full abstract»

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  • Accelerated Dynamic MRI Exploiting Sparsity and Low-Rank Structure: k-t SLR

    Publication Year: 2011 , Page(s): 1042 - 1054
    Cited by:  Papers (27)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1565 KB) |  | HTML iconHTML  

    We introduce a novel algorithm to reconstruct dynamic magnetic resonance imaging (MRI) data from under-sampled k-t space data. In contrast to classical model based cine MRI schemes that rely on the sparsity or banded structure in Fourier space, we use the compact representation of the data in the Karhunen Louve transform (KLT) domain to exploit the correlations in the dataset. The use of the data-... View full abstract»

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  • Computational Acceleration for MR Image Reconstruction in Partially Parallel Imaging

    Publication Year: 2011 , Page(s): 1055 - 1063
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1114 KB) |  | HTML iconHTML  

    In this paper, we present a fast numerical algorithm for solving total variation and ℓ1 (TVL1) based image reconstruction with application in partially parallel magnetic resonance imaging. Our algorithm uses variable splitting method to reduce computational cost. Moreover, the Barzilai-Borwein step size selection method is adopted in our algorithm for much faster convergence. Exp... View full abstract»

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  • A Fast Compressed Sensing Approach to 3D MR Image Reconstruction

    Publication Year: 2011 , Page(s): 1064 - 1075
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (4172 KB) |  | HTML iconHTML  

    The problem of high-resolution image volume reconstruction from reduced frequency acquisition sequences has drawn significant attention from the scientific community because of its practical importance in medical diagnosis. To address this issue, several reconstruction strategies have been recently proposed, which aim to recover the missing information either by exploiting the spatio-temporal corr... View full abstract»

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  • A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion

    Publication Year: 2011 , Page(s): 1076 - 1089
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1752 KB) |  | HTML iconHTML  

    We propose a novel statistical analysis method for functional magnetic resonance imaging (fMRI) to overcome the drawbacks of conventional data-driven methods such as the independent component analysis (ICA). Although ICA has been broadly applied to fMRI due to its capacity to separate spatially or temporally independent components, the assumption of independence has been challenged by recent studi... View full abstract»

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  • Compressed Sensing With Wavelet Domain Dependencies for Coronary MRI: A Retrospective Study

    Publication Year: 2011 , Page(s): 1090 - 1099
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (2851 KB) |  | HTML iconHTML  

    Coronary magnetic resonance imaging (MRI) is a noninvasive imaging modality for diagnosis of coronary artery disease. One of the limitations of coronary MRI is its long acquisition time due to the need of imaging with high spatial resolution and constraints on respiratory and cardiac motions. Compressed sensing (CS) has been recently utilized to accelerate image acquisition in MRI. In this paper, ... View full abstract»

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  • Spatially Regularized Compressed Sensing for High Angular Resolution Diffusion Imaging

    Publication Year: 2011 , Page(s): 1100 - 1115
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (2333 KB) |  | HTML iconHTML  

    Despite the relative recency of its inception, the theory of compressive sampling (aka compressed sensing) (CS) has already revolutionized multiple areas of applied sciences, a particularly important instance of which is medical imaging. Specifically, the theory has provided a different perspective on the important problem of optimal sampling in magnetic resonance imaging (MRI), with an ever-incre... View full abstract»

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  • Statistical Interior Tomography

    Publication Year: 2011 , Page(s): 1116 - 1128
    Cited by:  Patents (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (4681 KB) |  | HTML iconHTML  

    This paper presents a statistical interior tomography (SIT) approach making use of compressed sensing (CS) theory. With the projection data modeled by the Poisson distribution, an objective function with a total variation (TV) regularization term is formulated in the maximization of a posteriori (MAP) framework to solve the interior problem. An alternating minimization method is used to optimize t... View full abstract»

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  • Compressive Diffuse Optical Tomography: Noniterative Exact Reconstruction Using Joint Sparsity

    Publication Year: 2011 , Page(s): 1129 - 1142
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (845 KB) |  | HTML iconHTML  

    Diffuse optical tomography (DOT) is a sensitive and relatively low cost imaging modality that reconstructs optical properties of a highly scattering medium. However, due to the diffusive nature of light propagation, the problem is severely ill-conditioned and highly nonlinear. Even though nonlinear iterative methods have been commonly used, they are computationally expensive especially for three d... View full abstract»

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  • Sparsity-Driven Reconstruction for FDOT With Anatomical Priors

    Publication Year: 2011 , Page(s): 1143 - 1153
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (696 KB) |  | HTML iconHTML  

    In this paper we propose a method based on (2, 1)-mixed-norm penalization for incorporating a structural prior in FDOT image reconstruction. The effect of (2, 1)-mixed-norm penalization is twofold: first, a sparsifying effect which isolates few anatomical regions where the fluorescent probe has accumulated, and second, a regularization effect inside the selected anatomical regions. After formulati... View full abstract»

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  • Sparse Brain Network Recovery Under Compressed Sensing

    Publication Year: 2011 , Page(s): 1154 - 1165
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1243 KB) |  | HTML iconHTML  

    Partial correlation is a useful connectivity measure for brain networks, especially, when it is needed to remove the confounding effects in highly correlated networks. Since it is difficult to estimate the exact partial correlation under the small-n large-p situation, a sparseness constraint is generally introduced. In this paper, we consider the sparse linear regression model with a... View full abstract»

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  • Quality without compromise [advertisement]

    Publication Year: 2011 , Page(s): 1166
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    Publication Year: 2011 , Page(s): 1167
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    Publication Year: 2011 , Page(s): 1168
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  • IEEE Transactions on Medical Imaging Information for authors

    Publication Year: 2011 , Page(s): C3
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  • Blank page [back cover]

    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