Multi-channel, Multi-slice, and Multi-contrast Compressed Sensing MRI Using Weighted Forest Sparsity and Joint TV Regularization Priors

Datta, Sumit; Deka, Bhabesh

doi:10.1007/978-981-13-1592-3_65

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…In contrast, the proposed MC‐HTC approach exploits the low‐rank characteristic of multicontrast tensor for reconstruction without requiring calibration or additional prior information. Moreover, the low rankness enforced in the MC‐HTC method only identifies common linear relations of k‐space samples to recover missing data; thus, it is expected to be more resilient to slight interscan motion compared with those with strong modeling assumptions on sparsity patterns or locations of image structure 22,23 . In general, low‐rank and compressed sensing reconstruction are two categories of methods, and the MC‐HTC framework can be extended to incorporate spatial regularity 50 for further improvements.…”

Section: Discussionmentioning

confidence: 99%

“…Several recent multicontrast reconstruction approaches 22,23,49 have used image structural correlation based on compressed sensing and made improvements over single-contrast reconstruction. In contrast, the proposed MC-HTC approach exploits the low-rank characteristic of multicontrast tensor for reconstruction without requiring calibration or additional prior information.…”

Section: Comparison With Existing Multicontrast Reconstruction Apprmentioning

confidence: 99%

“…To further exploit this information redundancy, parallel imaging has been combined with compressed sensing reconstruction 13,14 for additional regularizations on similar or consistent structural edges across different contrasts. [15][16][17][18][19][20][21][22][23] Additionally, the highly correlated anatomical structure among multicontrast images ensures strong image-content similarity. This image structural correlation has been explored as a locally low-rank constraint, 24 demonstrating applications in parameter mapping, 25 dynamic imaging, 26 and multicontrast image denoising.…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods do not consider redundant structural information that is uniquely embedded in multicontrast datasets. To further exploit this information redundancy, parallel imaging has been combined with compressed sensing reconstruction 13,14 for additional regularizations on similar or consistent structural edges across different contrasts 15‐23 . Additionally, the highly correlated anatomical structure among multicontrast images ensures strong image‐content similarity.…”

Section: Introductionmentioning

confidence: 99%

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Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low‐rank Hankel tensor completion framework

Liu

Zhao

et al. 2021

Magnetic Resonance in Med

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Purpose: To jointly reconstruct highly undersampled multicontrast two-dimensional (2D) datasets through a low-rank Hankel tensor completion framework. Methods: A multicontrast Hankel tensor completion (MC-HTC) framework is proposed to exploit the shareable information in multicontrast datasets with respect to their highly correlated image structure, common spatial support, and shared coil sensitivity for joint reconstruction. This is achieved by first organizing multicontrast k-space datasets into a single block-wise Hankel tensor. Subsequent low-rank tensor approximation via higher-order singular value decomposition (HOSVD) uses the image structural correlation by considering different contrasts as virtual channels. Meanwhile, the HOSVD imposes common spatial support and shared coil sensitivity by treating data from different contrasts as from additional k-space kernels. The missing k-space data are then recovered by iteratively performing such low-rank approximation and enforcing data consistency. This joint reconstruction framework was evaluated using multicontrast multichannel 2D human brain datasets (T 1-weighted, T 2-weighted, fluid-attenuated inversion recovery, and T 1-weighted-inversion recovery) of identical image geometry with random and uniform undersampling schemes. Results: The proposed method offered high acceleration, exhibiting significantly less residual errors when compared with both single-contrast SAKE (simultaneous autocalibrating and k-space estimation) and multicontrast J-LORAKS (joint parallelimaging-low-rank matrix modeling of local k-space neighborhoods) low-rank reconstruction. Furthermore, the MC-HTC framework was applied uniquely to Cartesian uniform undersampling by incorporating a novel complementary k-space sampling | 3257 YI et al.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison With Existing Multicontrast Reconstruction Apprmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low‐rank Hankel tensor completion framework

Liu

Zhao

et al. 2021

Magnetic Resonance in Med

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“…But his technique has rather increased the average sampling ratio. Datta and Deka [10,45,46,[53][54][55] further explored iCS-MRI with different interpolation and reconstruction strategies. Although they reduced the computational cost of their interpolation algorithms [45,46] but they neither work on reducing the average sampling ratios nor on the consistency of their results.…”

Section: A Related Workmentioning

confidence: 99%

Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme

et al. 2020

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Compressed Sensing (CS) theory breaks the Nyquist theorem through random under-sampling and enables us to reconstruct a signal from 10%-50% samples. Magnetic Resonance Imaging (MRI) is a good candidate for application of compressed sensing techniques due to i) implicit sparsity in MR images and ii) inherently slow data acquisition process. In multi-slice MRI, strong inter-slice correlation has been exploited for further scan time reduction through interpolated compressed sensing (iCS). In this paper, a novel fast interpolated compressed sensing (FiCS) technique is proposed based on 2D variable density under-sampling (VRDU) scheme. The 2D-VRDU scheme improves the result by sampling the high energy central part of the k-space slices. The novel interpolation technique takes two consecutive slices and estimates the missing samples of the target slice (T slice) from its left slice (L slice). Compared to the previous methods, slices recovered with the proposed FiCS technique have a maximum correlation with their corresponding original slices. The proposed FiCS technique is evaluated by using both subjective and objective assessment. In subjective assessment, our proposed technique shows less partial volume loss compared to existing techniques. For objective assessment different performance metrics, such as structural similarity index measurement (SSIM), peak signal to noise ratio (PSNR), mean square error (MSE) and correlation, are used and compared with existing interpolation techniques. Simulation results on knee and brain dataset shows that the proposed FiCS technique has improved image quality and performance with even reduced scan time, lower computational complexity and maximum information content.

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Group-Sparsity Based Compressed Sensing Reconstruction for Fast Parallel MRI

Datta

Deka

2019

Lecture Notes in Computer Science

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Multi-channel, Multi-slice, and Multi-contrast Compressed Sensing MRI Using Weighted Forest Sparsity and Joint TV Regularization Priors

Cited by 7 publications

References 11 publications

Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low‐rank Hankel tensor completion framework

Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low‐rank Hankel tensor completion framework

Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme

Group-Sparsity Based Compressed Sensing Reconstruction for Fast Parallel MRI

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