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Abstract #2731

Super Resolution MRI using Multiple Signal Classification (MUSIC) Reconstruction

Dongbiao Sun1,2, Yan Zhuo1,2, Lin Chen2,3, and Zihao Zhang1,2,3
1Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, 2University of Chinese Academy of Sciences, Beijing, China, 3Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

Synopsis

Keywords: Vascular, Blood vessels, Super-Resolution

Motivation: The Fourier transform (FT) reconstruction has convenient implementation and stable performance; however, it has the problem of poor resolving power.

Goal(s): Our goal is to bypass the Fourier transform to obtain MR images, thereby solving the problem of poor resolution and achieving super-resolution imaging.

Approach: We were inspired by array signal processing theory and proposed an approach based on the Multiple Signal Classification (MUSIC) algorithm called MUSIC-MRI.

Results: Our phantom experiments suggest that the resolution ability of MUSIC-MRI is approximately 2x2 better than that of the 2D Fourier transform. Our in-vivo vascular imaging experiments show that the MUSIC-MRI significantly promotes the actual resolution.

Impact: MUSIC-MRI can break through the Rayleigh Limit of Fourier transform and significantly increase the actual resolution ability of the reconstructed images. Scientists or clinicians may use MUSIC-MRI to image very small structures and lesions without modifying MR sequences.

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