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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