Abstract #3478

K-space refinement method for DL-based MR reconstruction by regularizing k-space null space constraint with auto-calibrated kernel

Kanghyun Ryu¹, Cagan Alkan², and Shreyas S. Vasanawala¹

¹Radiology, Stanford University, Stanford, CA, United States, ²Electrical Engineering, Stanford, Stanford, CA, United States

Synopsis

In this study, we propose a novel refinement method using auto-calibrated k-space null-space kernel to reduce the k-space errors and enable reconstruction of improved high-frequency image details and textures. The refinement algorithm can be easily plugged in after DL-based reconstructions. 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.

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