Automated Detection of Suboptimal Fat Suppression - A Simulation Study

Motivation: Fat suppression on MR images is not always completely successful. Identification of inadequate fat suppression can be difficult for technologists while they are multitasking. Unidentified low quality images inhibit radiologists’ ability to diagnose. Goal(s): This work develops an automatic method to detect inadequate fat suppression in extremity MRI exams. Approach: Two-point Dixon fat-water image pairs were combined to simulate varying degrees of fat suppression failure and serve as training data for a CNN. Results: Greater than 85% accuracy was obtained on simulated data, which motivates future effort on prospective validation study. Impact: This work may lead to on-scanner software that auto-identifies inadequate fat suppression, prompting repeat scans with more refined shimming or alternative fat suppression methods. This may improve image quality.