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Fast and ultra-high shot diffusion MRI image reconstruction with self-adaptive Hankel subspace
Journal article   Peer reviewed

Fast and ultra-high shot diffusion MRI image reconstruction with self-adaptive Hankel subspace

Chen Qian, Mingyang Han, Liuhong Zhu, Zi Wang, Feiqiang Guan, Yucheng Guo, Dan Ruan, Yi Guo, Taishan Kang, Jianzhong Lin, …
Medical image analysis, Vol.102, 103546
05/2025
DOI: 10.1016/j.media.2025.103546
PMCID: PMC13379935
PMID: 40120287

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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. [Display omitted]
 High-resolution DWI Image reconstruction Low-rank Subspace

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