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Abstract #2601

Robust subspace-based parallel imaging (SPAN): Bridging sensitivity maps and convolution kernels

Tao Zu1 and Yi Zhang1
1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China

Synopsis

Keywords: Parallel Imaging, Parallel Imaging

Motivation: The reconstruction algorithms that rely on explicit sensitivity maps, like SENSE, are not robust, and prone to producing artifacts.

Goal(s): Our goal was to devise a novel, robust reconstruction method that is less susceptible to the inaccuracy of sensitivity maps.

Approach: The proposed method employs convolution kernels, derived from the singular value decomposition of block-wise Hankel matrix constructed from the k-space of sensitivity maps, to reconstruct under-sampled data.

Results: The proposed approach demonstrated superior and more robust performance compared to the SENSE method.

Impact: Transforming sensitivity maps into convolution kernels for parallel imaging can enhance image quality and robustness, and provide a novel perspective on leveraging sensitivity maps for researchers.

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