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Abstract #2790

Low-Rank Representation Enhanced Implicit Neural Representation for Zero-Shot Reconstruction in Highly Accelerated 3D Multi-parametric MRI

Haonan Zhang1, Guoyan Lao1, and Hongjiang Wei1,2
1School of Biomedical Engineering, Shanghai 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: AI/ML Image Reconstruction, AI/ML Image Reconstruction, low-rank, subspace,quantitative MRI

Motivation: Existing 3D multi-parametric quantitative MRI techniques encounter notable challenges in reconstruction under highly undersampled scenarios, limiting their potential in applications.

Goal(s): To achieve reconstruction for multi-parametric quantitative maps under high acceleration factors (e.g., a 2.5-minute scan for whole-brain), shortening scan times.

Approach: With low-rank representation, preliminary weighted images were estimated from highly undersampled, high-dimensional k-space data and utilized to facilitate the reconstruction of quantitative maps.

Results: The results demonstrated that, even under high acceleration factors, our proposed method improved the quality of reconstructed quantitative maps and ensured accurate quantitation.

Impact: The proposed framework could reconstruct whole-brain T1, T2, T2* maps within just a 2.5-minute scan. This advancement holds clinical promise for tissue characterization and pathological assessment in neuroscience, and enables probability for higher resolution and additional contrasts in MRI sequences.

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Keywords