Super Resolution MRI using Multiple Signal Classification (MUSIC) Reconstruction

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.