Accelerating T2 mapping of the brain by integrating deep learning priors with low‐rank and sparse modeling. Issue 3 (29th September 2020)

• Record Type: Journal Article
• Authors: Meng, Ziyu; Guo, Rong; Li, Yudu; Guan, Yue; Wang, Tianyao; Zhao, Yibo; Sutton, Brad; Li, Yao; Liang, Zhi‐Pei
• Is Part Of: Magnetic resonance in medicine. Volume 85:Issue 3(2021)
• Page Start: 1455
• Page End: 1467
• Publication Date: 2020-09-29
• Subjects: deep learning -- low‐rank modeling -- quantitative imaging -- sparse modeling -- T2 mapping; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28526
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To accelerate T2 mapping with highly sparse sampling by integrating deep learning image priors with low‐rank and sparse modeling. Methods: The proposed method achieves high‐speed T2 mapping by highly sparsely sampling (k, TE)‐space. Image reconstruction from the undersampled data was done by exploiting the low‐rank structure and sparsity in the T2 ‐weighted image sequence and image priors learned from training data. The image priors for a single TE were generated from the public Human Connectome Project data using a tissue‐based deep learning method; the image priors were then transferred to other TEs using a generalized series‐based method. With these image priors, the proposed reconstruction method used a low‐rank model and a sparse model to capture subject‐dependent novel features. Results: The proposed method was evaluated using experimental data obtained from both healthy subjects and tumor patients using a turbo spin‐echo sequence. High‐quality T2 maps at the resolution of 0.9 × 0.9 × 3.0 mm 3 were obtained successfully from highly undersampled data with an acceleration factor of 8. Compared with the existing compressed sensing–based methods, the proposed method produced significantly reduced reconstruction errors. Compared with the deep learning–based methods, the proposed method recovered novel features better. Conclusion: This paper demonstrates the feasibility of learning T2 ‐weighted image priors for multiple TEs using tissue‐based deep learning and generalized series‐based learning. A new method was proposed to effectively integrate these image priors with low‐rank and sparse modeling to reconstruct high‐quality images from highly undersampled data. The proposed method will supplement other acquisition‐based methods to achieve high‐speed T2 mapping.