Optimizing CT Scan Geometries With and Without Gradients

TL;DR

This paper compares gradient-based and gradient-free optimization methods for correcting patient motion in CT scans, showing that gradient-based algorithms converge faster while maintaining robustness, offering a promising alternative for motion compensation.

Contribution

It demonstrates that gradient-based optimization algorithms are effective and faster alternatives to gradient-free methods for CT motion correction problems.

Findings

Gradient-based algorithms converge faster than gradient-free methods.

Both approaches have similar robustness to parameter variations.

Gradient-based methods are viable for practical CT motion compensation.

Abstract

In computed tomography (CT), the projection geometry used for data acquisition needs to be known precisely to obtain a clear reconstructed image. Rigid patient motion is a cause for misalignment between measured data and employed geometry. Commonly, such motion is compensated by solving an optimization problem that, e.g., maximizes the quality of the reconstructed image with respect to the projection geometry. So far, gradient-free optimization algorithms have been utilized to find the solution for this problem. Here, we show that gradient-based optimization algorithms are a possible alternative and compare the performance to their gradient-free counterparts on a benchmark motion compensation problem. Gradient-based algorithms converge substantially faster while being comparable to gradient-free algorithms in terms of capture range and robustness to the number of free parameters. Hence, gradient-based optimization is a viable alternative for the given type of problems.