Abstract #1075

Development and validation of a rapid robust 3D-MRF with fast online recon suitable for large-scale neuroscientific and clinical applications

Zihan Zhou^1,2, Xiaozhi Cao^1,3, Congyu Liao^1,3, Mark Nishimura³, Sophie Schauman^1,3, Mengze Gao¹, Mahmut Yurt³, Nan Wang^1,3, Maya Yablonski⁴, Zhitao Li¹, Bruno P. Soares⁵, Ali Syed¹, Adam Kerr³, Jason D. Yeatman^2,4,6, and Kawin Setsompop^1,3

¹Department of Radiology, Stanford university, Stanford, CA, United States, ²Graduate School of Education, Stanford University, Stanford, CA, United States, ³Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ⁴Division of Developmental Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States, ⁵Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States, ⁶Department of Psychology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Software Tools, Software Tools, Pipeline, MR Fingerprinting

Motivation: A number of promising multiparameter mapping approaches have been developed but are not yet in routine-use in clinical and neuroscientific settings.

Goal(s): To create a rapid and robust 3D-MRF acquisition/reconstruction package, suitable for large-scale neuroscientific and clinical applications.

Approach: 3D-MRF acquisition is developed for 1-mm whole-brain-mapping in 2.5-minutes, with build-in robustness to motion, and quantification bias from B0&B1 inhomogeneities. Highly-efficient reconstruction package is created for online generation of quantitative maps and synthesized-contrasts within 4 minutes of scan-completion, benchmarked using a consumer-grade GPU.

Results: The pipeline has been validated on over 100-scans performed across clinical and neuroscientific settings with plan for open-source distribution soon.

Impact: The distribution of such an acquisition/reconstruction package should help facilitate wide-spread deployment of multiparameter mapping.

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