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Abstract #0334

ACE-Net: AutofoCus-Enhanced Convolutional Network for Field Imperfection Estimation with application to high b-value spiral Diffusion MRI

Mengze Gao1, Zachary Shah2, Xiaozhi Cao1, Daniel Abraham2, Nan Wang1, and Kawin Setsompop1,2
1Department of Radiology, Stanford University, Stanford, CA, United States, 2Department of Electrical Engineering, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Artifacts, Brain, System imperfections: measurement & correction

Motivation: Spatiotemporal magnetic field variations from B0 inhomogeneity and eddy currents can be detrimental to rapid image-encoding schemes such as spiral, EPI and 3D-cones, resulting in undesirable image artifacts.

Goal(s): In this work, a data driven approach for automatic estimation of spatiotemporal field imperfections is developed by combining autofocus metrics with deep learning, and by leveraging a compact basis representation of the expected field imperfections.

Approach: The method was applied to single-shot spiral diffusion MRI at high b-values where accurate estimation of B0 and eddy were obtained.

Results: Resulting in high quality image reconstruction without need for additional external calibrations.

Impact: We show accurate automatic estimation of spatiotemporal B0 and eddy field imperfections, which enables high-quality high b-value spiral diffusion imaging without additional calibration scans/field-probe measurements, should also prove useful to several other rapid imaging schemes and applications.

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Keywords