MRI scanners make use of a radiofrequency (RF) field to excite atomic nuclei. This RF field also deposits energy which may heat up the patient. To safeguard the patient, maximum levels of energy deposition and, therefore, strict power limits are in place. However, the actual temperature rise distribution that results from this constrained power deposition is not known. Simulations indicate that the peak power deposition and temperature rise might be quite large. We do not know if these simulations are accurate. Ideally, we would like to be able to measure the RF-induced distribution of energy deposition and temperature rise. For this purpose, the department of biomedical engineering of the Eindhoven University of Technology, together with the University Medical Center Utrecht, have started a project to develop such methodologies.
Recently, we demonstrated an AI-based method where spatial distributions of the specific absorption rate (SAR) are predicted by a deep-learning network from the MRI-measurable magnetic component of the RF field (the so-called B1+ field, figure 1). This method to measure energy deposition needs to be extended and improved. Next to this, MRI scanners are actually able to measure temperature rise. This technique is called 'MR Thermometry'. The technique is mostly used for thermal therapies where the local temperature rise easily reaches 5° C. For our purposes, the sensitivity needs to be improved considerably as temperature rise within subjects in regular MRI scans is expected to be below 1° C. Recently obtained results for the upper leg are shown in figure 2. With these methodologies, validation of thermal simulations becomes within reach. Also, we will be able to verify current RF power limits in MRI. The final goal of the project is to measure energy deposition and temperature rise within 30 subjects undergoing MRI examinations.
Next to another PhD student at the UMC Utrecht, you will develop MRI methods to measure power deposition and temperature rise distributions. Methods will be developed and tested using MRI scanners at the UMC Utrecht. All developments are supported by electromagnetic and thermal simulations as well as AI-based processing of measured data. Anticipated challenges include dealing with B0 field disturbances, motion artifacts, thermally induced susceptibility changes and many more. The exact distribution of tasks between both PhD students will be determined based on interests and capabilities. You will be part of the
medical image analysis group of the department of biomedical engineering at the Eindhoven University of Technology. There will be a strong cooperation with the
computational imaging group of the UMC Utrecht.
Figure 1: SAR prediction from the B1+ field (=flip angle map) using a CNN
Figure 2: MR thermometry results in the upper leg in comparison to simulations.