Synthesizing VERDICT maps from standard diffusion mp-MRI data using GANs

PurposeVERDICT maps have shown promising results in clinical settings discriminating normal from malignant tissue and identifying specific Gleason grades non-invasively. However, the quantitative estimation of VERDICT maps requires a specific diffusion-weighed imaging (DWI) acquisition. In this study we investigate the feasibility of synthesizing VERDICT maps from DWI data from multi-parametric (mp)-MRI which is widely used in clinical practice for prostate cancer diagnosis. MethodsWe use data from 67 patients who underwent both mp-MRI and VERDICT MRI. We compute the ground truth VERDICT maps from VERDICT MRI and we propose a generative adversarial network (GAN)-based approach to synthesize VERDICT maps from mp-MRI DWI data. We use correlation analysis and mean squared error to quantitatively evaluate the quality of the synthetic VERDICT maps compared to the real ones. ResultsQuantitative results show that the mean values of tumour areas in the synthetic and the real VERDICT maps were strongly correlated while qualitative results indicate that our method can generate realistic VERDICT maps from DWI from mp-MRI acquisitions. ConclusionRealistic VERDICT maps can be generated using DWI from standard mp-MRI. The synthetic maps preserve important quantitative information enabling the exploitation of VERDICT MRI for precise prostate cancer characterization with a single mp-MRI acquisition.