Location: Technical University of Denmark, Copenhagen area, Denmark
Gross salary (pre-employer/employee tax): 4200 €/month
Mobility allowance (pre-employer/employee tax): 600 €/month
Family allowance (pre-employer/employee tax): 500 €/month (if applicable)
Start date: April 2018 (roughly)
Duration: 36 Months
Project description: Glioma is the most frequent primary brain tumor and a disease with poor prognosis. The standard treatment combines surgery, radio- and chemotherapy, delaying tumor progression by a limited time, but leading to significant neurological deficits for the patients.
Diagnosis and decisions about treatment rely in most cases on information obtained from multiparametric magnetic resonance (MR) imaging protocols that map tumor structures, such as the actively growing tumor, or the edema surrounding the inner core of the lesion. Often this is complemented by other scans, for example positron emission tomography (PET) scans, that map additional physiological properties of the surrounding of the lesion, such as blood flow, tissue microstructure, or metabolic maps. After treatment, these multiparametric imaging protocols are continued to monitor the response to therapy and to detect signs of progression.
So far, image information is only used to guide treatment in a rather rudimentary fashion, for instance by evaluating tumor progression by estimating merely the diameter of the visible lesion. The overall goal of this PhD project is to develop accurate and interpretable prediction models for brain tumor grading and tumor recurrence detection from joint PET/MRI scans, taking advantage of the increase in sensitivity and specificity of dynamic FET PET data compared to conventional MRI. The project will involve designing and implementing novel probabilistic models, as well as validating the developed tools on several retrospective studies of subjects that underwent radiochemotherapy.
The selected candidate will be able to take advantage of the unique set-up of the TRABIT network, in particular through secondments (external research stays) at Mediri and the Technical University of Munich (both in Germany), as well as network-wide training events. The research will be performed in close collaboration with the Rigshospitalet (Copenhagen University Hospital).
Research environment: This project is hosted in the Image Analysis and Computer Graphics section of the Department of Applied Mathematics and Computer Science at the Technical University of Denmark. The section consists of around 50 faculty, postdocs and PhD students working in the general area of image analysis and image synthesis. Medical image analysis is one of the core topics of the section, with a specific focus on the computational modeling and analysis of brain scans. We maintain strong connections to the Martinos Center for Biomedical Imaging at Harvard Medical School, and contribute regularly to its open source software suite for neuroimage analysis FreeSurfer.
Your profile: You should have a master's degree in physics, computer science, or electrical engineering, or a similar degree with an equivalent academic level. A genuine interest in probabilistic modeling, machine learning and/or signal processing is a must, as is the ability (or the willingness to learn) to program in C++, Matlab or Python. Prior exposure to medical imaging is not required. Ability to travel for secondments in the TRABIT network is essential.
Successful applications are subject to academic approval; the selected candidate will be enrolled in one of the PhD programs at DTU.