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Abstract #0653

Accelerating 3D High-Resolution Brain MR Fingerprinting at 0.55T: Balanced Free-Precession meets Deep Learning

Rudy Rizzo1, Zhongnan Liu2, Jesus Ernesto Fajardo Freites1, Tom Griesler1,3, Jesse Hamilton1,3, Yun Jiang1,3, and Nicole Seiberlich1,3
1Department of Radiology, University of Michigan, Ann Arbor, MI, United States, 2Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States, 3Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

Synopsis

Keywords: Low-Field MRI, Low-Field MRI, MR Fingerprinting

Motivation: 0.55T whole-body MR scanners may increase MRI accessibility. While lower SNR can lead to poor image quality, MRF with balanced gradients may leverage high SNR and benefit from lower B0 inhomogeneities.

Goal(s): to generate fast 3D high-resolution MRF brain acquisition at 0.55T with improved SNR.

Approach: Two 3D MRF acquisitions are implemented with balanced (bSSFP) and gradient-spoiled (FISP) schemes. Low-Rank is compared to a Deep-Image-Prior reconstruction, which embeds iterative intrinsic denoising. Scans were performed on phantoms and four healthy subjects.

Results: The DIP-bSSFP MRF framework results in good accuracy, small coefficients of variation and yields good noise suppression and delineation of fine structures.

Impact: MRF-bSSFP with DIP reconstruction enables fast high-resolution 3D quantitative T1 and T2 mapping at 0.55T in 3:50min, thus potentially enabling fast synthetic generation of contrast images with good noise suppression and delineation of fine structures.

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Keywords