  Solving the inverse problem is the key step in evaluating the capacity of a
physical model to describe real phenomena. In medical image computing, it
aligns with the classical theme of image-based model personalization.
Traditionally, a solution to the problem is obtained by performing either
sampling or variational inference based methods. Both approaches aim to
identify a set of free physical model parameters that results in a simulation
best matching an empirical observation. When applied to brain tumor modeling,
one of the instances of image-based model personalization in medical image
computing, the overarching drawback of the methods is the time complexity for
finding such a set. In a clinical setting with limited time between imaging and
diagnosis or even intervention, this time complexity may prove critical. As the
history of quantitative science is the history of compression, we align in this
paper with the historical tendency and propose a method compressing complex
traditional strategies for solving an inverse problem into a simple database
query task. We evaluated different ways of performing the database query task
assessing the trade-off between accuracy and execution time. On the exemplary
task of brain tumor growth modeling, we prove that the proposed method achieves
one order speed-up compared to existing approaches for solving the inverse
problem. The resulting compute time offers critical means for relying on more
complex and, hence, realistic models, for integrating image preprocessing and
inverse modeling even deeper, or for implementing the current model into a
clinical workflow.
