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Abstract #3282

M(RF)2 – Improving MRF encoding speed with tailored spatiotemporal excitation patterns

Yonatan Urman1, Daniel Abraham1, and Kawin Setsompop1,2
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: New Trajectories & Spatial Encoding Methods, Image Reconstruction, Pulse sequence design

Motivation: Current Magnetic Resonance Fingerprinting techniques have shown promising results but have room for improvement in terms of acquisition speed and resolution.

Goal(s): Explore the potential of a new spatiotemporal encoding dimension unique to MRF, leveraging tailored spatiotemporal excitation patterns.

Approach: Employ spatially and temporally varying excitation patterns to create distinct signal evolution dictionaries at different spatial locations and reconstruct using multiple subspaces. To address computational challenges, we introduce a clustering method that reduces the number of subspaces, making the problem tractable.

Results: 2D reconstruction experiments demonstrate the approach's potential and show enhanced accuracy, particularly in T2 reconstruction.

Impact: We propose tailored spatiotemporal excitation encoding for Magnetic Resonance Fingerprinting. By leveraging controlled excitation patterns, we create spatially varying dictionaries that enhance the encoding capabilities, allowing for faster and higher-resolution imaging.

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