Abstract #3252

An open implementation of FSL’s eddy with volume-to-volume artifact estimation for neuroimaging beyond diffusion MRI

Jon Haitz Legarreta¹, Elodie Savary^2,3, Christopher J. Markiewicz⁴, Ariel Rokem⁵, Martin Norgaard^6,7, and Oscar Esteban^2,3

¹Radiology, Brigham and Women's Hospital, Mass General Brigham, Harvard Medical School, Boston, MA, United States, ²Dept. Radiology, Lausanne University Hospital, Lausanne, Switzerland, ³University of Lausanne, Lausanne, Switzerland, ⁴Department of Psychology, Stanford University, Stanford, CA, United States, ⁵Department of Psychology, University of Washington, Seattle, WA, United States, ⁶Department of Computer Science, University of Copenhagen, Copenhagen, Denmark, ⁷Molecular Imaging Branch, National Institute of Mental Health, Bethesda, MD, United States

Synopsis

Keywords: Data Processing, Artifacts, neuroimaging, head motion, eddy currents, gaussian process, software

Motivation: Create an open-source alternative to FSL’s eddy without commercial limitations that generalizes over problems beyond eddy current distortions of diffusion MRI.

Goal(s): Evaluate the tool’s performance vis-à-vis with corresponding tools.

Approach: We implement a Gaussian Process regressor model, a Bayesian optimization layer on an existing registration framework and estimate the alignment of neuroimaging data enabled by the eddymotion framework.

Results: Using publicly available neuroimaging data, we provide evidence about the effectiveness of our volume-to-volume modeling framework for generalized artifact identification and correction in neuroimaging.

Impact: We present eddymotion, an open-source framework for volume-to-volume artifact estimation inspired by FSL eddy. Our tool allows for easy alternative model implementation, it can be used for non-dMRI imaging modalities and does not have a restrictive license.

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