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Abstract #3344

K-space Guided Under-sampling Pattern Optimization for Implicit Neural Representation-based Reconstruction

Mingxing Chen1 and Hongjiang Wei1,2
1Shanghai Jiao Tong University, Shanghai, China, 2National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Data Acquisition, Acquisition Methods

Motivation: Given a specific acceleration rate, there exist variable under-sampling patterns, which may lead to significant differences in reconstruction performance. It is unclear which under-sampling pattern is the most efficient for the given anatomy and reconstruction method employed.

Goal(s): This study aims to develop a data-driven and model-specific optimization approach to optimize under-sampling patterns, thereby improving MRI reconstruction quality.

Approach: We proposed an approach based on the stochastic greedy algorithm incorporated with k-space guidance for under-sampling pattern optimization.

Results: Our method demonstrated superior reconstruction quality, producing fewer artifacts compared to conventional patterns. The optimized patterns are data-dependent, varying with image anatomy and coil settings.

Impact: This study significantly improves MRI reconstruction quality by optimizing under-sampling patterns, potentially leading to more accurate clinical diagnoses. It opens avenues for further research on the adaptability of the under-sampling patterns in various clinical contexts, ultimately enhancing patient outcomes.

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