The precise characterization of aggressive brain tumors remains a challenging problem due to their highly heterogeneous radiographic and molecular presentation. The integration of mathematical models with clini- cal imaging data holds an enormous promise of developing robust predictive and explainable models that quantify cancer growth with the potential to as- sist in diagnosis and treatment. In general, such models are parameterized by many unknown parameters and their estimation can be formally posed as an inverse problem. However, this calibration problem is a formidable task for aggressive brain tumors due to the absence of longitudinal data, resulting in a strongly ill-posed inverse problem. This is further exacerbated by the inherent non-linearity in tumor growth models. Overcoming these difficulties involves the introduction of sophisticated regularization strategies along with compu- tationally efficient algorithms and software. Towards this end, we introduce a fully-automatic inversion framework which provides an entirely new capa- bility to analyze complex brain tumors from a single pretreatment magnetic resonance imaging (MRI) scan. Our framework employs fast algorithms and optimized implementations which exploit distributed-memory parallelism and GPU acceleration to enable reasonable solution times – an important factor for clinical applications. We validate our solver on clinical data and demonstrate its utility in characterizing important biophysics of brain cancer along with its ability to complement other radiographic information in downstream machine learning tasks
