Abstract

Motivation: Ultra-strong gradients scanners allow to explore microstructure, but these systems are not widespread because of their associated challenges. Goal(s): Our goal is to leverage image synthesis and deep learning to design a neural network able to predict high b-values data from low b-values. Approach: .We implemented a U-net architecture and tailored a loss function able to learn tissue-based features in a patch-based fashion. We trained it on a large dataset and tested it quantitatively and qualitatively. Results: .Qualitative and quantitative results showed a remarkable agreement between synthetic high-b values and the ground-truth. A preliminary test with a microstructural model also gave encouraging results. Impact: Being able to synthesise high b-value data from clinical data could unleash the availability of advanced microstructural models to study the human brain and body, with applications in fundamental research and in the clinical settings.