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Abstract #1195

Machine Learning-Optimized Sampling Enables High-Resolution 3D MR Fingerprinting for Dynamic Quantification of Solute Transport in CSF

Yuran Zhu1, Guanhua Wang2, Junqing Zhu3, Rasim Boyacioglu1, Chris A. Flask1,3,4, and Xin Yu1,3,5
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 3Department of Radiology, Case Western Reserve University, Cleveland, OH, United States, 4Department of Pediatrics, Case Western Reserve University, Cleveland, OH, United States, 5Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH, United States

Synopsis

Keywords: MR Fingerprinting, MR Fingerprinting, Imaging Neurofluids, Contrast Agent

Motivation: Enhance spatiotemporal resolution of MR fingerprinting to dynamically track solute transport in cerebrospinal fluid.

Goal(s): Increase undersampling capacity of MR fingerprinting by optimizing the sampling patterns for a stack-of-spiral trajectory.

Approach: We developed a machine learning-based method to optimize rotation angles for spiral arms across kz partitions and time frames in 3D MRF acquisition. Optimized sampling pattern were tested in simulation studies and in vivo experiments.

Results: An additional 2-fold undersampling was achieved, enabling simultaneous mapping of T1 and T2 across the whole mouse brain with 200-µm isotropic resolution in 4.3 min, allowing dynamically tracking contrast agents in CSF while maintaining anatomical detail.

Impact: We present a novel method to leverage machine learning to improve sampling for MR fingerprinting. This method achieves simultaneous mapping of T1 and T2 in the whole mouse brain at 200-µm isotropic resolution in 4.3 min.

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