K-space refinement in deep learning MR reconstruction via regularizing scan specific SPIRiT-based self consistency

Kanghyun Ryu, Cagan Alkan, Chanyeol Choi, Ikbeom Jang, Shreyas Vasanawala

Abstract: Deep Learning (DL) based reconstruction using unrolled neural networks has shown great potential in accelerating magnetic resonance imaging (MRI). However, one of the major drawbacks is the loss of high-frequency details and textures in the output. In this paper, we propose a novel refinement method based on SPIRiT (Iterative Self-consistent Parallel Imaging Reconstruction from Arbitrary k-Space) formulation to reduce the k-space errors and enable reconstruction of improved high-frequency image details and textures. The proposed scheme constrains the DL output to satisfy the neighborhood relationship in the frequency space (k-space) which can be easily calibrated in the auto-calibration (ACS) lines, and corrects the underestimation in the peripheral region of the k-space as well as reduce structured k-space errors. We show that our method enables the reconstruction of sharper images with significantly improved high-frequency components measured by HFEN and GMSD while maintaining overall error in the image measured by PSNR and SSIM.

External IDs:dblp:conf/iccvw/RyuACJV21