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Abstract #2777

ZSSPI : Zero-Shot Scan Specific Parallel Imaging MRI Reconstruction using Attention based mechanism

Jinho Joo1, Hyeseong Kim1,2, Taejoon Eo1,3, Hyeyeon Won1, and Dosik Hwang1,2,4,5
1School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, Seoul, Korea, Republic of, 3Probe Medical, Seoul, Korea, Republic of, 4Radiology, Yonsei University College of Medicine, Seoul, Korea, Republic of, 5Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea, Republic of

Synopsis

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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