A Model-Based Deep Network for MRI Reconstruction Using Approximate Message Passing Algorithm

Qiao, Xiaoyu; Du, Jinglong; Wang, Lulu; He, Zhongshi; Jia, Yuanyuan

doi:10.1109/icassp40776.2020.9053131

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…46,49 AMP was originally developed for linear systems, 39,41 the standard AMP has been used with success to recover MR images from linear k-space measurements. [50][51][52] Rich et al [53][54][55] later designed a nonlinear AMP framework for phase-contrast MRI and 4D flow imaging.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic Bayesian approach to recover R2*$$ {R}_{2\ast } $$ map and phase images for quantitative susceptibility mapping

Huang

Lah

Allen

et al. 2022

Magnetic Resonance in Med

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Purpose Undersampling is used to reduce the scan time for high‐resolution three‐dimensional magnetic resonance imaging. In order to achieve better image quality and avoid manual parameter tuning, we propose a probabilistic Bayesian approach to recover R2∗$$ {R}_2^{\ast } $$ map and phase images for quantitative susceptibility mapping (QSM), while allowing automatic parameter estimation from undersampled data. Theory Sparse prior on the wavelet coefficients of images is interpreted from a Bayesian perspective as sparsity‐promoting distribution. A novel nonlinear approximate message passing (AMP) framework that incorporates a mono‐exponential decay model is proposed. The parameters are treated as unknown variables and jointly estimated with image wavelet coefficients. Methods Undersampling takes place in the y‐z plane of k‐space according to the Poisson‐disk pattern. Retrospective undersampling is performed to evaluate the performances of different reconstruction approaches, prospective undersampling is performed to demonstrate the feasibility of undersampling in practice. Results The proposed AMP with parameter estimation (AMP‐PE) approach successfully recovers R2∗$$ {R}_2^{\ast } $$ maps and phase images for QSM across various undersampling rates. It is more computationally efficient, and performs better than the state‐of‐the‐art l1$$ {l}_1 $$‐norm regularization (L1) approach in general, except a few cases where the L1 approach performs as well as AMP‐PE. Conclusion AMP‐PE achieves better performance by drawing information from both the sparse prior and the mono‐exponential decay model. It does not require parameter tuning, and works with a clinical, prospective undersampling scheme where parameter tuning is often impossible or difficult due to the lack of ground‐truth image.

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Section: Introductionmentioning

confidence: 99%

A probabilistic Bayesian approach to recover R2*$$ {R}_{2\ast } $$ map and phase images for quantitative susceptibility mapping

Huang

Lah

Allen

et al. 2022

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…To overcome this problem [16,21,23,29,39], some methods were introduced to accelerate the MRI measuring in kspace called Compressed Sensing MRI (CS-MRI) [3,6,8,12,18,25,27,31,38]. Due to the fact that random measurement in kspace is generally inappropriate [2] as the measurement matrix, some measurement matrix designing methods have been developed for MRI reconstruction [4,5,13,19,24,32,33]. More recently, in the spirit of growing success in learning-based data acquisition, there has been significant interest in learning measurement matrix [2,10,34].…”

Section: Introductionmentioning

confidence: 99%

MRI Measurement Matrix Learning via Correlation Reweighting

Zhang

Chen

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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In Compressive Sensing MRI (CS-MRI), measurement matrix learning has been developed as a promising method for measurement matrix designing. Research on MRI measurement task suggests that Relative 2-Norm Error (RLNE) of measurement images is imbalanced. However, current learning-based investigations suffer from the lack of probing imbalanced characteristic on measurement matrix learning. In this paper, we propose a novel Measurement Matrix Learning via Correlation Reweighting (MML-CR) approach for exploring and solving this problem by optimizing reweighted model. Specifically, we introduce a reweighting expected minimization model to obtain an essential measurement matrix in k-space. Besides, we propose an example correlation regularizer to prevent trivial solution for learning weights. Furthermore, we present an alternating solution and perform convergence analysis for the optimization. We also demonstrate quantitative and qualitative experimental results which show that our algorithm outperforms several state-of-art measurements methods. Compared with conventional methods, MML-CR achieves better performance on universal task. CCS CONCEPTS • Computing methodologies → Reconstruction.

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A Model-Based Deep Network for MRI Reconstruction Using Approximate Message Passing Algorithm

Cited by 2 publications

References 13 publications

A probabilistic Bayesian approach to recover R2*$$ {R}_{2\ast } $$ map and phase images for quantitative susceptibility mapping

A probabilistic Bayesian approach to recover R2*$$ {R}_{2\ast } $$ map and phase images for quantitative susceptibility mapping

MRI Measurement Matrix Learning via Correlation Reweighting

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