Deep learning with synthetic diffusion MRI data for free-water elimination in glioblastoma cases

Abstract

Glioblastoma is the most common and aggressive brain tumor. In clinical practice, diffusion MRI (dMRI) enables tumor infiltration assessment, tumor recurrence prognosis, and identification of white-matter tracks close to the resection volume. However, the vasogenic edema (free-water) surrounding the tumor causes partial volume contamination, which induces a bias in the estimates of the diffusion properties and limits the clinical utility of dMRI. We introduce a voxel-based deep learning method to map and correct free-water partial volume contamination in dMRI. Our model learns from synthetically generated data a non-parametric forward model that maps free-water partial volume contamination to volume fractions. This is independent of the diffusion protocol and can be used retrospectively. We show its benefits in glioblastoma cases: first, a gain of statistical power; second, quantification of free-water and tissue volume fractions; and third, correction of free-water contaminated diffusion metrics. Free-water elimination yields more relevant information from the available data.