Keywords: Diffusion Analysis & Visualization, Susceptibility, Distortion correction, Nonrigid registration, Susceptibility distortion, Deep learning
Motivation: Diffusion MR images suffer from susceptibility distortion artifacts due to field inhomogenities and susceptibility changes at tissue interfaces. This results in a spatial mismatch of the MR signal. Current methods either use blip-up, blip-down acquisitions and/or are compute-intensive.
Goal(s): Our goal is to develop a fast registration-based susceptibility distortion correction method without the need to acquire an opposite phase-encode scan.
Approach: We use synthesized b0 volumes generated using synb0 as a registration target for SyN registration to correct susceptibility distortion.
Results: Our distortion correction method produces results that are qualitatively and quantitatively similar to state-of-the-art methods in a fraction of the time.
Impact: Our approach serves as a good starting point to explore registration based distortion correction methods. Faster correction methods will enable widespread use of dMRI in the clinical setting where accurate shape is needed for critical decisions like treatment planning.
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