Prostate cancer is the most common tumor in men and the second leading cause of death after lung cancer. Unfortunately, blood tests give only an indirect hint on prostate cancer probability. The current gold standard for prostate cancer diagnosis is the invasive biopsy, which is uncomfortable and can miss the tumor lesion. MRI is a noninvasive way to obtain 3D prostate images for diagnostics and surgical planning. However, the contrast of prostate MRI is poor. A complementary imaging modality remains an unmet medical need.

To overcome these challenges, this MSc project will use microstructural MRI, a developing branch of MRI that provides biological information well beyond the actual imaging resolution. For example, we were the first to evaluate the average caliber of brain microvasculature in patients, which is about 10 μm (compared to a 200fold coarser image resolution of 2 mm) [1]. The paradigm of microstructural MRI is to create a biophysical model con-necting the tissue properties with specifically prepared MRI signal. After the measurement, the tissue properties (e.g., cell size) can be calculated from the signatures of the MR signal.
In this thesis a new method for prostate cancer detection will be developed using time-dependent water diffusion [2]. Healthy prostate tissue includes fluid-filled spaces (lumina, cf. Fig. 1) which are too small to be imaged. In MRI these spaces create a slowly attenuating signal component, and their diffusion-hindering boundaries result in a specific signature in the measured tissue diffusion coefficient [3]. The aim of this study is to investigate whether the geometry of the fluid-filled spaces is an indicator of tissue com-pression by a tumor.