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Abstract #5221

A Patch-Based Hankel Low-Rank Method for MRI Reconstruction

Xinlin Zhang1, Hengfa Lu2, Yiman Huang3, Lixuan Rao1, Tao Tan4, and Tong Tong1
1College of Physics and Information Engineering and Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China, 2Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, United States, 3College of Electrical Engineering, Zhejiang University, Zhejiang, China, 4Faulty of Applied Science, Macao Polytechnic University, Macao Special Administrative Region of China, China

Synopsis

Keywords: Image Reconstruction, AI/ML Image Reconstruction

Motivation: Hankel low-rank reconstruction methods have shown the ability to produce high-quality reconstructions with lower reconstruction error and robustness to sampling patterns. However, their high computational complexity poses a significant challenge, particularly in high-dimensional imaging scenarios, limiting their applicability in situations requiring fast processing.

Goal(s): We aim at accelerating Hankel low-rank reconstruction without sacrificing reconstruction quality.

Approach: In this work, a patch-based Hankel low rank method is proposed by utilizing the low-rankness of a series of k-space patches.

Results: Experimental results demonstrate our proposed scheme achieves approximately 4$$$\times$$$ acceleration compared to the traditional Hankel low-rank methods while maintaining comparable reconstruction errors.

Impact: The proposed scheme has the flexibility to potentially improve all Hankel low-rank methods across various applications, providing fast and high-quality MRI reconstruction.

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