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Improvements in magnetic resonance imaging using information redundancy
, 2005
"... This thesis describes a number of algorithms related to the acquisition, reconstruction and postprocessing of Magnetic Resonance data. The basic theme underlying each of these algorithms is the use of a unified systems approach to exploit information redundancy available in MR imaging. There are ..."
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This thesis describes a number of algorithms related to the acquisition, reconstruction and postprocessing of Magnetic Resonance data. The basic theme underlying each of these algorithms is the use of a unified systems approach to exploit information redundancy available in MR imaging. There are three basic contributions. The first concerns the development of a new motion correction algorithm for TimeResolved MR Angiography. Motion artifacts in angiography data are very difficult to remove without affecting vascular evolution. Our algorithm uses successive POCS iterations to remove unwanted artifacts without degrading quality. Doubleblind testing has indicated significant improvement over angiograms created manually by experienced radiologist. In summary, our method seeks to exploit temporal redundancy to remove motion artifacts. The second contribution is our recent work on Parallel MR imaging in presence of sensitivity errors using a Maximum Likelihood technique. It can be shown that standard phased array reconstruction using popular parallel imaging methods is inappropriate in presence of errors in measuring sensitivity maps of coils. Since
FAST REGULARIZED RECONSTRUCTION OF NONUNIFORMLY SUBSAMPLED PARALLEL MRI DATA
"... Parallel MR imaging is an effective approach to reduce MR image acquisition time. Nonuniform subsampling allows one to tailor the subsampling scheme for improved image quality at high acceleration factors. However, nonuniform subsampling precludes fast reconstruction schemes such as SENSE, and is ..."
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Parallel MR imaging is an effective approach to reduce MR image acquisition time. Nonuniform subsampling allows one to tailor the subsampling scheme for improved image quality at high acceleration factors. However, nonuniform subsampling precludes fast reconstruction schemes such as SENSE, and is more likely to require a regularized solution than reconstruction of uniformly subsampled data demands. This means that one needs to choose a good regularization parameter, typically requiring multiple expensive system solves. Here, we present an efficient LSQRHybrid algorithm which simultaneously addresses the need for rapid regularization parameter selection and fast reconstruction. This algorithm can reconstruct nonuniformly subsampled parallel MRI data, with automatic regularization and good image quality, in a time competitive with Cartesian SENSE. 1.
ROBUST SOLVERS FOR INVERSE IMAGING PROBLEMS USING DENSE SINGLEPRECISION HARDWARE
"... Abstract. We present an iterative framework for robustly solving large inverse problems arising in imaging using only singleprecision (or other reducedprecision) arithmetic, which allows the use of highdensity processors (e.g. Cell BE and Graphics Processing Units). Robustness here means linearcon ..."
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Abstract. We present an iterative framework for robustly solving large inverse problems arising in imaging using only singleprecision (or other reducedprecision) arithmetic, which allows the use of highdensity processors (e.g. Cell BE and Graphics Processing Units). Robustness here means linearconvergence even for large problems (billions of variables), with high levels of noise (signal to noise levels less than unity). This framework handles problems formulated as quadratic and general nonlinear minimization problems. Sparse and dense problems can be treated, as long as there are efficient parallelizable matrixvector products for the transformations involved. Outer iterations correspond to approximate solutions of a quadratic minimization problem, using a single Newton step. Inner iterations correspond to the estimation of the step via truncated Neumann series or minimax polynomial approximations built from operator splittings. Given the simple convergence analysis, this approach can also be used in embedded environments with fixed computation budgets, or certification requirements, like realtime medical imaging. We describe a benchmark problem from MRI, and a series of penalty functions suited to this framework. An important family of such penalties is motivated by both Bilateral Filtering and Total Variation, and we show how they can be optimized using linear programming. We also discuss penalties designed to segment images, and use different types of a priori knowledge, and show numerically that the different penalties are effective when used in combination. 1.
DOI 10.1155/IJBI/2006/49378 Progressive Magnetic Resonance Image Reconstruction Based on Iterative Solution of a Sparse Linear System
, 2005
"... Image reconstruction from nonuniformly sampled spatial frequency domain data is an important problem that arises in computed imaging. Current reconstruction techniques suffer from limitations in their model and implementation. In this paper, we present a new reconstruction method that is based on so ..."
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Image reconstruction from nonuniformly sampled spatial frequency domain data is an important problem that arises in computed imaging. Current reconstruction techniques suffer from limitations in their model and implementation. In this paper, we present a new reconstruction method that is based on solving a system of linear equations using an efficient iterative approach. Image pixel intensities are related to the measured frequency domain data through a set of linear equations. Although the system matrix is too dense and large to solve by direct inversion in practice, a simple orthogonal transformation to the rows of this matrix is applied to convert the matrix into a sparse one up to a certain chosen level of energy preservation. The transformed system is subsequently solved using the conjugate gradient method. This method is applied to reconstruct images of a numerical phantom as well as magnetic resonance images from experimental spiral imaging data. The results support the theory and demonstrate that the computational load of this method is similar to that of standard gridding, illustrating its practical utility. Copyright © 2006 Yasser M. Kadah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
Accelerating Dynamic Spiral MRI by Algebraic Reconstruction From Undersampled k–t Space
"... Abstract—The temporal resolution of dynamic magnetic resonance imaging (MRI) can be increased by sampling a fraction ofspace in an interleaved fashion, which introduces spatial and temporal aliasing. We describe algebraically and graphically the aliasing process caused by dynamic undersampled spira ..."
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Abstract—The temporal resolution of dynamic magnetic resonance imaging (MRI) can be increased by sampling a fraction ofspace in an interleaved fashion, which introduces spatial and temporal aliasing. We describe algebraically and graphically the aliasing process caused by dynamic undersampled spiral imaging within 3D space (the Fourier transform of space) and formulate the unaliasing problem as a set of independent linear inversions. Since each linear system is numerically underdetermined, the use of prior knowledge in the form of bounded support regions is proposed. To overcome the excessive memory requirements for handling large matrices, a fast implementation of the conjugate gradient (CG) method is used. Numerical simulation and in vivo experiments using spiral twofold undersampling demonstrate reduced motion artifacts and the improved depiction of fine cardiac structures. The achieved reduction of motion artifacts and motion blur is comparable to simple filtering, which is computationally more efficient, while the proposed algebraic framework offers greater flexibility to incorporate additional algebraic acceleration techniques and to handle arbitrary sampling schemes. Index Terms—Algebraic reconstruction, dynamic imaging, fast imaging, spiral cardiac magnetic resonance imaging (MRI), temporal acceleration. I.
Temporal Regularization Use in Dynamic ContrastEnhanced MRI
, 2011
"... I am incredibly grateful to so many people for all of the love, support, and guidance I have received over the years. Of course, I’d like to thank my advisor, Jeff Fessler, who, for better or for worse, convinced me to go to grad school in the first place. He inspired me as an undergrad, and he insp ..."
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I am incredibly grateful to so many people for all of the love, support, and guidance I have received over the years. Of course, I’d like to thank my advisor, Jeff Fessler, who, for better or for worse, convinced me to go to grad school in the first place. He inspired me as an undergrad, and he inspires me still today. I think it’s safe to say that this dissertation wouldn’t exist without Jeff’s endless guidance, patience, and support. Thank you. I also want to thank the other members of my committee: Doug Noll, who first taught me the nuts and bolts of MRI, and whose group meetings and group members were an important part of my graduate education; Tom Chenevert, whose insights and knowledge regarding “real life ” DCEMRI were invaluable to this research project; and
ON THE REGULARIZATION OF SENSE AND SPACERIP IN PARALLEL MR IMAGING
"... Parallel imaging methods provide accelerated multiple coil MR image acquisitions via reconstruction of subsampled kspace data. Currently, analytic comparison between different reconstruction approaches has been hampered by use of different phase encoding paradigms and regularization approaches, hi ..."
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Parallel imaging methods provide accelerated multiple coil MR image acquisitions via reconstruction of subsampled kspace data. Currently, analytic comparison between different reconstruction approaches has been hampered by use of different phase encoding paradigms and regularization approaches, historically unique to each method. We present an analysis of the SpaceRIP image reconstruction problem that demonstrates the ability to recast the problem in a decoupled form when uniform downsampling is employed. We show that this decoupled problem is equivalent to the SENSE image reconstruction approach. This approach enables a clear analytic comparison between SENSE and SpaceRIP, and we demonstrate the effect of different regularization approaches on image formation in low coil sensitivity regions. 1.
ACCELERATED 3D MRI OF VOCAL TRACT SHAPING USING COMPRESSED SENSING AND PARALLEL IMAGING
"... 3D MRI of the upper airway has provided valuable insights into vocal tract shaping and data for the modeling of speech production. Small movements of articulators can lead to large changes in the produced sound, therefore improving the resolution of these datasets, within the constraints of a sustai ..."
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3D MRI of the upper airway has provided valuable insights into vocal tract shaping and data for the modeling of speech production. Small movements of articulators can lead to large changes in the produced sound, therefore improving the resolution of these datasets, within the constraints of a sustained sound (612 seconds), is an important area for investigation. This paper provides the first application of compressed sensing (CS) with parallel imaging to highresolution 3D upper airway MRI. We use spatial finite difference as the sparsifying transform, and investigate the use of highresolution phase information as a constraint during CS reconstruction. In a retrospective subsampling experiment with no sound production, 5x undersampling produced acceptable image quality when using phaseconstrained CS reconstruction. The prospective use of this accelerated acquisition enabled 3D vocaltract MRI during sustained production of English /s/,/�/,/i/,/r / with 1.33x1.33x1.33mm 3 spatial resolution and 10seconds of scan time. Index Terms — speech production, compressed sensing MRI, vocal tract shaping, sensitivity encoding, phase constraint. 1.