Quantitative water content mapping is a promising technique to monitor brain diseases. However, established protocols suffer from long acquisition times and sensitivity to patient motion. To overcome these issues, we propose to use golden angle radial MRI and model-based iterative reconstruction. This allows accurate and precise estimation of water content and T1 values, while providing significantly higher motion robustness, as shown in phantom and in vivo experiments. The golden angle-based k-space sampling allows for a nearly optimal k-space coverage even in the event of early termination of measurement. This opens the opportunity to apply such a technique in the clinic.