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Abstract #4987

Towards Accelerated Low-Field Multi-Contrast MRI with Anatomy-Aware Deep Unrolling

Yuzhu He1, Fan Wang2, Fangmao Ju1, Ruyi Xiao2, chunfeng lian1, and Jianhua Ma2
1School of Mathematics and Statistics, Xi'an Jiatong University, Xi'an, China, 2Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi'an, China

Synopsis

Keywords: Low-Field MRI, Low-Field MRI, Image Reconstruction, Multi-contrast

Motivation: Low-field multi-contrast MRI (MC-MRI) faces challenges with low image quality, long scanning times and a disconnect between existing imaging procedure and specific clinical needs.

Goal(s): Our goal is to efficiently reconstruct high-quality MC-MR images from highly undersampled multi-contrast low-field MRI k-space data, tailored to meet specific clinical requirements.

Approach: Our proposed task-oriented reconstruction model integrates learnable group sparsity and anatomy-aware regularization. The model's iterative algorithm is then unfolded into a deep network (A2MC-MRI), which jointly learn k-space sampling patterns to enhance clinically relevant regions.

Results: Our A2MC-MRI outperforms existing methods, particularly in improving the imaging quality of clinically relevant targets under high acceleration.

Impact: The A2MC-MRI offers a promising approach for fast MC-MRI reconstruction with enhanced task-specific imaging quality, particularly for clinically relevant anatomical regions. This method has the potential to support downstream clinical applications by catering to individual patient needs.

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Keywords