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Current treatment planning of patients diagnosed with a brain tumor could benefit from assessment of the spatial distribution of tumor cell concentration. Magnetic resonance imaging (MRI) can reveal areas of high cell density in gliomas, but areas of low cell concentration, which can serve as a source for the secondary appearance of the tumor after treatment, are not detected. 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. In this work, the Menze group introduces Learn-Morph-Infer: a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. 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. As such, the proposed inverse solution approach allows for clinical translation of brain tumor personalization and could also be adopted to other scientific and engineering domains.
See Ezhov et al., Med Image Anal.