Neuroimaging whether for clinical diagnosis, patient stratification or for discovering biomarkers involves trade-offs when collecting data. For example, with a typical MRI scan, there are substantial practical constraints (money, patient comfort and compliance, radiological reporting) which mean decisions have to be taken as to what kind of scan to perform, where in the brain to scan, and the resolution of scan.
The standard approach is to make these decisions before scanning commences, acquiring the data then analysing it. However, the optimal scanning protocol will depend on what is being investigated and the type and location of pathology/abnormalities and is often not known prior to scanning or variable depending on the patient.
In this PhD project, we propose developing an alternative framework in which data is analysed in near real-time to optimise which sequences are acquired: so-called ‘active acquisition’. This will have a number of potential benefits for the use or MRI in clinical and scientific studies: firstly, acquisition time is reduced, improving patient comfort and thus compliance, reducing costs and the collection of unwanted data. Secondly, data acquisition is much more flexible, with potentially unanticipated sequences acquired that could aid clinical decision-making. In addition, adaptive acquisition provides an alternative approach for finding neural biomarkers in highly heterogeneous clinical populations. The project will develop the image acquisition and real-time analysis methodology as well as provide a proof-of-principle that active acquisition can be used in real-time to generate custom scanning protocols for accurate individualised prediction. Follow-up studies will employ active acquisition in clinical groups, to individually optimise data collection for diagnosis, prognosis and clinical trial recruitment, thereby implementing precision medicine for neurological and psychiatric diseases.