A Two Step Workflow to Support Fully Autonomous MR Scanning in Prostate

Motivation: A fully automatic workflow for scan plane prescription is desirable in clinical settings. Goal(s): Our goal is to demonstrate a deep learning-based MRI scan workflow for fully automated MR scanning in the prostate. Approach: This new scan workflow will identify anatomical landmarks and scan planes for prostate planning (coverage, FOV and orientation) from coil sensitivity and 3plane scout images. Results: The deep learning-based anatomy recognition showed acceptable average location error below 5mm and plane orientation error below 10 degrees. Impact: As no interaction from the operator is required to complete a full MR prostate scan, it paves the way for fully automated MR scans for the prostate anatomy.