Journal article
Fast and ultra-high shot diffusion MRI image reconstruction with self-adaptive Hankel subspace
Medical image analysis, Vol.102, 103546
05/2025
DOI: 10.1016/j.media.2025.103546
PMID: 40120287
Abstract
•We propose DONATE, a novel 1D low-rank reconstruction method with adaptive subspace, for fast and ultra-high shot DWI.•DONATE can accomplish 4-shot DWI reconstruction in 10 seconds, and enable over 10 times faster computation in the 12-shot reconstruction than conventional 2D low-rank method.•DONATE shows superiority on low-distortion spine DWI reconstruction and best subjective image quality evaluation scores by 4 radiologists.•DONATE has potential to provide fast and reliable multi-shot DWI reconstructions, which have higher resolution and lower geometry distortion.
Multi-shot interleaved echo planar imaging is widely employed for acquiring high-resolution and low-distortion diffusion weighted images (DWI). These DWI images, however, are easily affected by motion artifacts induced by inter-shot phase variations which could be removed by enforcing the low-rankness of a huge 2D block Hankel matrix of the k-space. Successful applications have been evidenced on 4∼8 shots DWI but failure was observed on ultra-high shots, e.g. 10∼12 shots, limiting the extension to higher-resolution DWI. Moreover, the 2D Hankel matrix reconstruction is very time-consuming. Here, we propose to accelerate the reconstruction through decomposing this huge 2D matrix into small 1D lOw-raNk HAnkel (DONA) matrices from every k-space readout line. This extension encounters another problem of variant low-rankness across the k-space. To address this issue, we propose to separate signal subspaces of 1D Hankel matrices into the strong and uncertain ones. The former is pre-estimated from an initial image to reduce the degree of freedom in reconstruction. The latter protects image details in reconstruction by avoiding the overshadowing on small singular values. This method is called DONA with self-adapTive subspacE estimation (DONATE). In vivo results show that DONATE can not only accomplish 4-shot reconstruction in 10 seconds but also the reconstruction of 12 shots with 10 times faster computation. Besides, DONATE shows superiority on low-distortion spine DWI reconstruction and subjective image quality evaluation in terms of blind scoring by 4 radiologists.
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Details
- Title: Subtitle
- Fast and ultra-high shot diffusion MRI image reconstruction with self-adaptive Hankel subspace
- Creators
- Chen Qian - Xiamen UniversityMingyang Han - Xiamen UniversityLiuhong Zhu - Zhongshan Hospital of Xiamen UniversityZi Wang - Xiamen UniversityFeiqiang Guan - Xiamen UniversityYucheng Guo - Xiamen UniversityDan Ruan - Xiamen UniversityYi Guo - Zhongshan Hospital of Xiamen UniversityTaishan Kang - Zhongshan Hospital of Xiamen UniversityJianzhong Lin - Zhongshan Hospital of Xiamen UniversityChengyan Wang - Fudan UniversityMerry Mani - University of IowaMathews Jacob - University of IowaMeijin Lin - Xiamen UniversityDi Guo - Xiamen University of TechnologyXiaobo Qu - Xiamen UniversityJianjun Zhou - Zhongshan Hospital of Xiamen University
- Resource Type
- Journal article
- Publication Details
- Medical image analysis, Vol.102, 103546
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.media.2025.103546
- PMID
- 40120287
- ISSN
- 1361-8415
- eISSN
- 1361-8423
- Grant note
- National Natural Science Foundation of China: 62331021, 62371410, 62122064 Natural Science Foundation of Fujian Province of China: 2023J02005, 2022J011425 Industry-University Cooperation Projects of the Ministry of Education of China: 231107173160805 National Key R & D Program of China: 2023YFF0714200 Zhou Yongtang Fund for High Talents Team: 0621-Z0332004 President Fund of Xiamen University: 20720220063 Xiamen University Nanqiang Outstanding Talents Program
The authors thank reviewers for insightful comments, which greatly improve this work. The authors also thank Qiaoli Yao, Anjie Xie, Jingkui Pei, Boyu Jiang, Ran Tao, and Zhigang Wu for their assistance in data acquisition; Zhangren Tu, and Hua Guo for discussions on the ssDWI reconstructions; Justin P. Haldar from University of Southern California; Rodrigo A. Lobos from University of Michigan for discussion; Qinrui Cai for assistance in running MATLAB code. This work was supported in part by the National Natural Science Foundation of China (62331021, 62371410, and 62122064) , the Natural Science Foundation of Fujian Province of China under grant (2023J02005, and 2022J011425) , Industry-University Cooperation Projects of the Ministry of Education of China (231107173160805) , National Key R & D Program of China (2023YFF0714200) , Zhou Yongtang Fund for High Talents Team (0621-Z0332004) , the President Fund of Xiamen University (20720220063) , and the Xiamen University Nanqiang Outstanding Talents Program.
- Language
- English
- Electronic publication date
- 03/14/2025
- Date published
- 05/2025
- Academic Unit
- Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute
- Record Identifier
- 9984802210402771
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