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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