3D imaging using magnetic resonance tomosynthesis (MRT) technique

Kim, Min Oh; Zho, Sang Young; Kim, Dong Hyun

doi:10.1118/1.4737111

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…This general forward model involving sum of convolutions is encountered in many imaging problems such as different three-dimensional image reconstruction problems (K ≥ 1, S ≥ 1) in computational imaging [8,9,[11][12][13][14][15][16][17][18][19], as well as classical and multiframe image deconvolution (K ≥ 1, S = 1). Note that our model allows each blurring operator to have a different weight as commonly used in the literature; for simplicity, these weights are simply embedded into the terms h k,s 's in our model.…”

Section: Forward Problemmentioning

confidence: 99%

“…In this paper, we focus on the solution of this type of inverse problems, which are called here convolutional inverse problems [10]. Such inverse problems are encountered in various computational imaging modalities such as computational photography, wide-field astronomical imaging, three-dimensional microscopy, spectral imaging, ultrafast imaging, radio interferometric imaging, magnetic resonance imaging, and ultrasound imaging [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Algorithms for Convolutional Inverse Problems in Multidimensional Imaging

Dogan,

Oktem

2020

Preprint

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Multidimensional imaging, capturing image data in more than two dimensions, has been an emerging field with diverse applications. Due to the limitation of two-dimensional detectors in obtaining the high-dimensional image data, computational imaging approaches have been developed to pass on some of the burden to a reconstruction algorithm. In various image reconstruction problems in multidimensional imaging, the measurements are in the form of superimposed convolutions. In this paper, we introduce a general framework for the solution of these problems, called here convolutional inverse problems, and develop fast image reconstruction algorithms with analysis and synthesis priors. These include sparsifying transforms, as well as convolutional or patch-based dictionaries that can adapt to correlations in different dimensions. The resulting optimization problems are solved via alternating direction method of multipliers with closed-form, efficient, and parallelizable update steps. To illustrate their utility and versatility, the developed algorithms are applied to three-dimensional image reconstruction problems in computational spectral imaging for cases with or without correlation along the third dimension. As the advent of multidimensional imaging modalities expands to perform sophisticated tasks, these algorithms are essential for fast iterative reconstruction in various large-scale problems.

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Section: Forward Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Algorithms for Convolutional Inverse Problems in Multidimensional Imaging

Dogan,

Oktem

2020

Preprint

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“…These methods achieved through-plane acceleration. A similar gradient scheme can be found in the view angle tilting (VAT) technique ( 14 , 15 ), where, in addition to the separation of multiple slices, chemical shift artifacts caused by excitation can be shifted back to their original position during RO. However, the additional gradient causes voxel shearing, which results in blurring as a tradeoff.…”

Section: Introductionmentioning

confidence: 99%

MultiSlice CAIPIRINHA Using View Angle Tilting Technique (CAIPIVAT)

2016

Self Cite

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We aim to focus on improving the performance of slice parallel imaging while simultaneously correcting for spatial shift artifacts related to off-resonance. In multislice controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA), simultaneously excited slices are shifted along the phase-encoding direction by varying the radiofrequency phase for each slice, thereby obtaining virtually shifted coil sensitivity information. Meanwhile, the view angle tilting (VAT) technique provides additional shifts in the readout direction to further spread an image overlap while correcting for field inhomogeneity-induced spatial misregistration using a compensation gradient. By combining these features of CAIPIRINHA and VAT, named CAIPIVAT, the excited individual slices are shifted along both phase-encoding and readout directions. Consequently, the number of aliased voxels is reduced, and the virtual coil sensitivity information is more effectively used. Blurring due to the compensation gradient in VAT was alleviated by using a constrained least square filter. The advantages of CAIPIVAT are shown by signal-to-noise ratio simulation, phantom experiments, and in vivo experiments. Thus, CAIPIVAT can be useful for multislice parallel imaging while providing the correction of off-resonance-related spatial shift artifact.

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3D imaging using magnetic resonance tomosynthesis (MRT) technique

Cited by 2 publications

References 22 publications

Efficient Algorithms for Convolutional Inverse Problems in Multidimensional Imaging

Efficient Algorithms for Convolutional Inverse Problems in Multidimensional Imaging

MultiSlice CAIPIRINHA Using View Angle Tilting Technique (CAIPIVAT)

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