Towards Automated Deep Learning based MR-only Radiation Therapy Planning in <5mins MR Scan Time

Motivation: Deep Learning (DL) is an enabling technology for MR-only Radiation Therapy Planning in terms of 1) MR to synthetic CT conversion and 2) automated Organ-At-Risk (OAR) segmentation. Goal(s): To investigate the feasibility of DL based MR-only RT planning using accelerated MR protocols with <5mins scan time (not including tumor depiction). Approach: Quantitative evaluation of synthetic CTs in terms of mean absolute error (MAE) and OAR segmentations in terms of Likert Score analysis. Results: Preliminary results demonstrate the feasibility of DL-based MR-only RT in <5min scan time with only minor degradation of synthetic CT and OAR segmentation quality. Impact: Deep Learning is an enabling technology for MR-only Radiation Therapy Planning. Here we demonstrate its capabilities for 1) synthetic CT conversion and 2) Organ-At-Risk (OAR) segmentation in <5min MR scan time.