Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

Abstract

Current treatment planning of patients diagnosed with brain tumor could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, such as magnetic-resonance imaging (MRI), contrast sufficiently well areas of high cell density. However, they do not portray areas of low concentration, which can often serve as a source for the secondary appearance of the tumor after treatment.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 that prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a methodology for inferring patient-specific spatial distribution of brain tumor 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.

Casting the inverse problem as a database query. The case of personalized tumor growth modeling

“…The synthetic dataset was generated by randomly sampling patient-specific parameters from the subsequent ranges and feeding the resulting parameter sets θ = {x, y, z, D w , ρ, T end } to the reaction diffusion model. Analogous to [25], we discarded unrealistic in size tumors based on the range of real tumor sizes (BraTS dataset [26]).…”

Towards Image - Based Personalization of Glioblastoma Therapy A Clinical and Biological Validation Study of a Novel, Deep Learning - Driven Tumor Growth Model

“…model parameters best describing an individual patient's tumor 15 . In brief, a patient's tumor segmentation is morphed into the SRI24 atlas space 30 using ANTs.…”

“…In this work, we implement a novel model that has recently been introduced by our working group 15 . This model applies a neural network -based methodology that shows potential for clinical implementation by estimating the individual tumor growth parameters only from preoperative MR standard imaging in a matter of minutes.…”