Recovering Quantitative Maps from Conventional Weighted MR Images using Deep Learning

Abstract

Long acquisition times impede the routine clinical use of quantitative magnetic resonance imaging (qMRI). qMRI quantifies meaningful tissue parameters in T1-, T2-, and PD-maps, as opposed to conventional (qualitative) weighted MRI (wMRI), which only visualises contrast between tissues. Although methods exist that generate synthetic wMRI from qMRI, the inverse problem has not been thoroughly studied yet. A method to generate qMRI from wMRI would be beneficial as it does not change current clinical workflows and enables retrospective quantitative analysis. This thesis investigates to what extent fully convolutional networks are successful in generating qMRI from T1-weighted, T2-weighted, PD-weighted and T2-weighted-FLAIR scans. A set of synthetic wMRI scans from 97 healthy volunteers was split into training, validation and test sets for development of our models. We varied model architectures, loss functions and learning rates during training, in order to find the best performing models. These were able to predict qMRI with median errors of approximately 5% on the test set. Additionally, we determined the amount of information contained in the input scans by training models using different combinations of the input. These results showed that T1-weighted, T2-weighted and PD-weighted scans were the most important. Models trained on synthetic wMRI were tested on an additional dataset of real wMRI. This resulted in higher median errors of 27.4%, 12.0% and 8.7% for T1-, T2- and PD-maps respectively. Furthermore, the same models were tested on a third dataset of synthetic tumour scans and mainly showed errors around the tumour core. These results show that more research is necessary in order to improve the performances of models generating qMRI to a clinical standard.