Deep learning-based automated scan planning for brain MRI

Motivation: Manual scan planning in clinical MRI is inaccurate, inconsistent and time-consuming. Goal(s): A deep learning-based end-to-end automated scan planning framework has been developed for MRI head scans. Approach: We propose a two-stage end-to-end 3D cascaded convolutional network framework, called 3D CFP-UNet, which localizes the positions of five key anatomical landmarks and achieves a coarse-to-fine result. We also propose loss functions PRL and DRL with physical meaning in automatic scan planning. Results: Our approach yields satisfactory scan planning results on 229 test subjects, with PAE and PRE reaching 0.872mm and 0.10%, respectively. Impact: MRI automated scan planning can help improve scan efficiency. Also, it improves scan consistency for follow-up comparisons.