J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (3): 499-509.doi: 10.1007/s12204-023-2647-2
收稿日期:
2022-08-10
接受日期:
2022-12-24
出版日期:
2025-06-06
发布日期:
2025-06-06
段继忠,徐昱含,黄欢
Received:
2022-08-10
Accepted:
2022-12-24
Online:
2025-06-06
Published:
2025-06-06
摘要: 结构化低秩并行磁共振成像模型在自校准信号受限的情况下,可以有效地重构出磁共振图像,但其计算量大导致重构时间长,而难以得到临床应用。为了提高并行磁共振图像的重构质量和重构速度,将联合稀疏变换学习(JTL)与SAKE结构化低秩重构模型结合,使用交替方向乘子法对模型进行求解,并引入优化梯度法提高收敛速度。另外,使用图形处理器进行加速,从而得到一种并行磁共振成像快速重构算法JTLSAKE。通过对四组人类活体的并行磁共振成像数据集进行重构实验可以看出,提出的JTLSAKE算法可以获得与基于JTL的PLORAKS算法相当的重构质量,并且重构速度提高了43倍以上,重构200×200 pixels的8通道磁共振图像仅需4 s。
中图分类号:
. 基于变换学习和结构化低秩模型的并行成像快速重构算法[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 499-509.
Duan Jizhong, Xu Yuhán, Huang Huan. Fast Parallel Magnetic Resonance Imaging Reconstruction Based on Sparsifying Transform Learning and Structured Low-Rank Model[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 499-509.
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