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Abstract #2786

KOMET: Kspace Optimal Masking for Efficient Training of Zero-shot MRI Reconstruction

Hyeseong Kim1,2, Jinho Joo1, Deukhee Lee2,3,4, Dosik Hwang1,5,6,7, and Taejoon Eo1,8
1School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2Center for Bionics, Korea Institute of Science and Technology, Seoul, Korea, Republic of, 3Division of AI Robotics, KIST School, University of Science and Technology, Seoul, Korea, Republic of, 4Yonsei-KIST Convergence Research Institute, Yonsei University, Seoul, Korea, Republic of, 5Radiology, Yonsei University College of Medicine, Seoul, Korea, Republic of, 6Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea, Republic of, 7Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, Seoul, Korea, Republic of, 8Probe Medical, Seoul, Korea, Republic of

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, zeroshot mri reconstruction, Masked Image Modeling, scan-specific mri reconstruction

Motivation: Zero-shot learning shows promise in various domains but remains underexplored in MRI reconstruction due to inherent challenges in recovering images without supervision of fully sampled data.

Goal(s): To develop a stable zero-shot MRI reconstruction framework eliminating the need for fully sampled reference data.

Approach: We propose a dynamic k-space masking strategy inspired by masked image modeling, coupled with joint optimization of image reconstruction and coil sensitivity estimation.

Results: KOMET shows robust performance across various undersampling patterns (4×, 8× reduction with Gaussian and uniform masks) on FastMRI knee dataset, outperforming traditional parallel imaging methods and recent zero-shot approach with 2dB PSNR improvement.

Impact: KOMET establishes a novel framework for stable zero-shot MRI reconstruction by adapting masked modeling to k-space domain. This advancement enables robust acceleration without fully sampled reference data, paving the way for broader clinical application of accelerated MRI.

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Keywords