  Current treatment planning of patients diagnosed with a brain tumor, such as
glioma, could significantly benefit by accessing the spatial distribution of
tumor cell concentration. Existing diagnostic modalities, e.g. magnetic
resonance imaging (MRI), contrast sufficiently well areas of high cell density.
In gliomas, however, they do not portray areas of low cell concentration, which
can often serve as a source for the secondary appearance of the tumor after
treatment. To estimate tumor cell densities beyond the visible boundaries of
the lesion, numerical simulations of tumor growth could complement imaging
information by providing estimates of full spatial distributions of tumor
cells. Over recent years a corpus of literature on medical image-based tumor
modeling was published. It includes different mathematical formalisms
describing the forward tumor growth model. Alongside, various parametric
inference schemes were developed to perform an efficient tumor model
personalization, i.e. solving the inverse problem. However, the unifying
drawback of all existing approaches is the time complexity of the model
personalization which prohibits a potential integration of the modeling into
clinical settings. In this work, we introduce a deep learning based methodology
for inferring the patient-specific spatial distribution of brain tumors from
T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer the method
achieves real-time performance in the order of minutes on widely available
hardware and the compute time is stable across tumor models of different
complexity, such as reaction-diffusion and reaction-advection-diffusion models.
We believe the proposed inverse solution approach not only bridges the way for
clinical translation of brain tumor personalization but can also be adopted to
other scientific and engineering domains.
