High quality diffusion images from accelerated acquisition on an MR-Linac by using Deep learning.

Motivation: Trade-offs between acquisition time and precision of the apparent diffusion coefficient (ADC) hinder the adoption of diffusion-weighted (DW) MRI for biologically-adaptive MR-guided radiotherapy. Goal(s): To obtain high quality DW images and precise ADC maps using deep learning while shortening acquisition times on an MR-Linac. Approach: We trained U-net models to generate high quality DW images and ADC maps from only one average per b-value. Four models were trained using either trace-weighted or single DW direction images and with or without registration to the b0 image. Results: Trained models effectively generated high-quality images from subsampled data. Registration reduced ADC variability. Impact: Using deep learning we obtained high quality diffusion-weighted MRI from subsampled MR-Linac data. Shortened acquisitions and increased precision of the apparent diffusion coefficient facilitate integration into adaptive MR-guided radiotherapy workflows that use diffusion-weighted MRI for treatment response assessment and prediction.