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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