Fast multi-modal imaging for diagnosis and therapy assessment of tumour patients

Malignant gliomas are aggressive tumours with poor prognosis. Taking into account the aggressive nature of these tumours and the wide age range at which they occur, malignant gliomas have a considerable socio-economic impact. Magnetic resonance imaging (MRI) is the imaging modality of choice in diagnosis, aggressiveness assessment and follow-up of these tumours. However, in the management of malignant gliomas current imaging techniques still lack in diagnostic accuracy.
In this project, we aim to develop an imaging protocol in which different advanced MR techniques are combined within an examination time acceptable for patients. Furthermore, our goal is to develop (automated) data processing algorithms which address the following areas: (a) establish an accurate diagnosis, (b) make an early prognosis on success of therapy, (c) identify areas of microscopic tumour infiltration, (d) identify mechanisms that contribute to success and failure of (new) therapeutic interventions.
Considering the diversity of imaging and spectroscopic data being collected for each patient over time, fundamental computational problems arise, such as reliable extraction of meaningful features from low quality spectroscopic data, or optimal combination of all available data sources for robust classification. Novel data analysis methods will be developed in this project, such as metabolic feature extraction with 3D spatial prior knowledge, and multi-modal approaches to 3D nosologic imaging.