Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Zero shot Scan-specific mri reconstruction, regularization method, attention mechanism
Motivation: Existing zero-shot MRI reconstruction methods struggle to impose effective constraints on non-sampled k-space regions, leading to degraded reconstruction quality.
Goal(s): To improve reconstruction quality by effectively handling non-sampled k-space regions.
Approach: Introduce AKSM (Attention-based K-space Selective Mechanism) that selectively focuses on critical nonsampled k-space regions with indirect constraints.
Results: Our experiments on the FastMRI brain dataset [1] demonstrate superior performance across multiple acceleration factors (4×, 8×) and sampling patterns (Gaussian, uniform), consistently outperforming existing zero-shot methods [2] in both reconstruction quality and computational efficiency.
Impact: Our AKSM enables robust zero-shot MRI reconstruction by effectively utilizing undersampled k-space data. This breakthrough allows for significant scan time reduction without compromising image quality, potentially transforming clinical practice and making advanced MRI more accessible to patients.
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