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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