# PDE-constrained optimization in medical image analysis

## TL;DR

This paper reviews PDE-constrained optimization in medical image analysis, focusing on formulation, discretization, and numerical solutions for applications like image registration and data assimilation in neuroimaging and cardiovascular imaging.

## Contribution

It provides a comprehensive overview of current techniques and challenges in solving PDE-constrained optimization problems in medical imaging, highlighting state-of-the-art computational methods.

## Key findings

- Effective regularization and preconditioning are crucial for solving high-dimensional PDE systems.
- Distributed-memory architectures are often necessary for practical computation.
- State-of-the-art numerical methods can address ill-posedness and computational complexity.

## Abstract

PDE-constrained optimization problems find many applications in medical image analysis, for example, neuroimaging, cardiovascular imaging, and oncological imaging. We review related literature and give examples on the formulation, discretization, and numerical solution of PDE-constrained optimization problems for medical imaging. We discuss three examples. The first one is image registration. The second one is data assimilation for brain tumor patients, and the third one data assimilation in cardiovascular imaging. The image registration problem is a classical task in medical image analysis and seeks to find pointwise correspondences between two or more images. The data assimilation problems use a PDE-constrained formulation to link a biophysical model to patient-specific data obtained from medical images. The associated optimality systems turn out to be sets of nonlinear, multicomponent PDEs that are challenging to solve in an efficient way. The ultimate goal of our work is the design of inversion methods that integrate complementary data, and rigorously follow mathematical and physical principles, in an attempt to support clinical decision making. This requires reliable, high-fidelity algorithms with a short time-to-solution. This task is complicated by model and data uncertainties, and by the fact that PDE-constrained optimization problems are ill-posed in nature, and in general yield high-dimensional, severely ill-conditioned systems after discretization. These features make regularization, effective preconditioners, and iterative solvers that, in many cases, have to be implemented on distributed-memory architectures to be practical, a prerequisite. We showcase state-of-the-art techniques in scientific computing to tackle these challenges.