Development of novel methods for obtaining robust dynamic susceptibility contrast magnetic resonance imaging biomarkers from diseased brain in children

Dynamic susceptibility contrast (DSC-) MRI is an important imaging technique from which estimates of perfusion measures including cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) can be calculated. These perfusion measures can be used to indicate health in a range of diseases. However, acquisition protocol varies from centre-to-centre, which leads to variability in data quality between centres and limits the clinical applicability of DSC-MRI. Currently, the recommended process for assessing data quality is by eye, which is very time consuming and subjective between reviewers. In this work an automated processing pipeline for DSC-MRI was produced. Work to develop the pipeline demonstrated that data quality of DSC-MRI data can be assessed using machine learning classifiers, which were trained using metrics calculated from the data and the results of qualitative review. It also showed that it was possible to denoise the data using singular value decomposition (SVD) based methods, which were validated on a simulator and confirmed in patient data. The pipeline created was applied to a multicentre patient dataset where it demonstrated the importance of denoising DSC-MRI data in improving data quality and how data quality can vary with acquisition protocol. It was also applied to a single centre study of patients receiving differing treatments for brain tumours and suggested there are no significant changes in relative CBV (rCBV) in non-tumour brain between differing treatment groups. The pipeline developed during this work has wider applications in other imaging modalities and could be adapted to be applied to other perfusion imaging methods, such as dynamic contrast enhanced (DCE-) MRI, or any other imaging modality that involves analysis of a signal variation with time, such as computed tomography (CT) perfusion imaging or positron emission tomography (PET)