We're building deep-learning models to detect and remove motion and noise artifacts from MR images — making scans clearer without repeat imaging.
A physics-informed, deep-learning pipeline for retrospective motion correction in MRI — reducing artifacts caused by patient movement so radiologists get diagnostic-quality images on the first pass.