Abstract Non-invasive, multi-parametric characterization of prostate cancer (PC), with magnetic resonance imaging (MRI) methods, is an active area of research with great potential for providing improved diagnosis and treatment monitoring. The PI-RADSv2 assessment system that was established by an international team of experts recognizes the value of quantitative images in PC diagnosis, but relies largely on qualitative evaluation of weighted images. Although this grading approach achieves reasonably good separation be- tween normal and abnormal prostate tissue, it does not achieve adequate separation between indolent and aggressive disease, with the risk that more unnecessary and costly surgery is performed with poten- tially dire consequences on patient quality of life. High-value protocols, without need for an invasive and costly endo-rectal radio-frequency coil are being investigated. This comes at the cost of extended scan time and reduced image quality in terms of spatial resolution, signal-to-noise ratio and signal bias, which negatively impacts sensitivity and specificity of multi-parametric MRI. With the pronounced increase of multi-parametric MRI exams, there is also the desire to integrate the support by the most recent revolution in diagnostic imaging, i.e., machine learning. It becomes increasingly clear, that in order to avoid having to train neural networks for each specific system and protocol, reproducible and thus preferably quantitative imaging protocols are essential. To overcome these limitations, we propose both pulse sequence develop- ment, investigation of ADC validity and reproducibility and novel post-processing strategies. The overall objective is to demonstrate the added value of lesion characterization with quantitative values and at the same time understand and minimize the influence of protocol choices and scan hardware, hence improve overall reproducibility. Specific Aim 1 will focus on the development of a low distortion MR imaging sequence for rapid concurrent quantification of T2 and diffusion signal decay. Specific Aim 2 will examine ADC variations that result from changes in diffusion time over a range that is typical with present day clinical MR systems. Specific Aim 3 introduces advanced handling of low noise diffusion data, which will be indispensable for achieving high accuracy and precision with non-invasive and economic external coils. Specific Aim 4 introduces a novel ADC computation approach that fully captures the complex diffusion signal decays in tissues and at the same time is largely protocol and system independent. Moreover, re- sulting images and quantitative maps processed according this approach, exhibit considerably lower noise, which can be traded for higher spatial resolution or shorter scan duration. In combination, the consis- tently quantitative nature of the data and its ubiquitous validity and comparability will greatly facilitate the establishment of recommendations for disease-related thresholds....