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Solving the inverse problem is the key step in evaluating the capacity of aphysical model to describe real phenomena. In medical image computing, italigns with the classical theme of image-based model personalization.Traditionally, a solution to the problem is obtained by performing eithersampling or variational inference based methods. Both approaches aim toidentify a set of free physical model parameters that results in a simulationbest matching an empirical observation. When applied to brain tumor modeling,one of the instances of image-based model personalization in medical imagecomputing, the overarching drawback of the methods is the time complexity forfinding such a set. In a clinical setting with limited time between imaging anddiagnosis or even intervention, this time complexity may prove critical. As thehistory of quantitative science is the history of compression, we align in thispaper with the historical tendency and propose a method compressing complextraditional strategies for solving an inverse problem into a simple databasequery task. We evaluated different ways of performing the database query taskassessing the trade-off between accuracy and execution time. On the exemplarytask of brain tumor growth modeling, we prove that the proposed method achievesone order speed-up compared to existing approaches for solving the inverseproblem. The resulting compute time offers critical means for relying on morecomplex and, hence, realistic models, for integrating image preprocessing andinverse modeling even deeper, or for implementing the current model into aclinical workflow.

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling