Optimizing CT Scan Geometries With and Without Gradients

Thies M, Wagner F, Maul N, Pfaff L, Schneider LS, Syben C, Maier A (2023)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2023

Event location: Stony Brook University, New York

Open Access Link: https://arxiv.org/abs/2302.06251

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.

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How to cite

APA:

Thies, M., Wagner, F., Maul, N., Pfaff, L., Schneider, L.-S., Syben, C., & Maier, A. (2023). Optimizing CT Scan Geometries With and Without Gradients. In Proceedings of the 17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D). Stony Brook University, New York, US.

MLA:

Thies, Mareike, et al. "Optimizing CT Scan Geometries With and Without Gradients." Proceedings of the 17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), Stony Brook University, New York 2023.

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