Accelerated Glioma characterization with VERDICT MRI: a comparison between deep learning and non-linear least squares fitting

Motivation: Complex multi-compartment models of diffusion MRI, as the recent adaptation of VERDICT-MRI for brain tumours, can provide important microstructural information, but traditional fitting is time-consuming and may not be accurate. Goal(s): To explore the feasibility of deep-learning-based fitting of VERDICT for brain tumours. Approach: We fit the VERDICT model to data from 15 glioma patients using both traditional and deep-learning approaches. We compared the resulting parameters between the two methods and with histology. Results: VERDICT estimates from deep-learning and traditional fitting showed a good correlation and reflected histology features. The deep-learning fitting was much faster once the model was trained. Impact: We have successfully used deep learning to fit the complex VERDICT model for brain tumour microstructure. As deep-learning fitting is much faster and potentially more precise than traditional methods, this could facilitate the clinical application of VERDICT for brain tumours.