Runtime optimization of acquisition trajectories for x-ray computed tomography with a robotic sample holder

Abstract

Tomographic imaging systems are expected to work with a wide range of samples that house complex structures and challenging material compositions, which can influence image quality in a bad way. Complex samples increase total measurement duration and may introduce beam-hardening artifacts that lead to poor reconstruction image quality. This work presents an online trajectory optimization method for an x-ray computed tomography system with a robotic sample holder. The proposed method reduces measurement time and increases reconstruction image quality by generating an optimized spherical trajectory for the given sample without prior knowledge. The trajectory is generated successively at runtime based on intermediate sample measurements. We present experimental results with the robotic sample holder where two sample measurements using an optimized spherical trajectory achieve improved reconstruction quality compared to a conventional spherical trajectory. Our results demonstrate the ability of our system to increase reconstruction image quality and avoid artifacts at runtime when no prior information about the sample is provided.

Algorithms in Tomography and Related Inverse Problems—A Review

“…This is crucial as it addresses the real issue of optimizing image acquisition for specific diagnostic purposes, highlighting the importance of tailored imaging strategies. The authors Pekel, Lavilla et al [45] lead us into the field of optimizing X-ray CT trajectories. Customizing imaging paths for specific samples addresses the practical challenge of efficient data acquisition and high-quality image production, coordinating the need for precision in tomographic imaging.…”

elsa: an elegant framework for tomographic reconstruction

“…elsa has been integral to our group's research and vital to our scientific publications. Noteworthy application examples include robotic X-ray CT and trajectory optimization (Pekel et al, 2022a(Pekel et al, , 2022b(Pekel et al, , 2023, as well as successful integration with deep learning approaches (Cheslerean-Boghiu et al, 2023). Recognized in scientific conferences (Frank et al, 2023;Lasser et al, 2019), elsa is an indispensable tool, contributing significantly to advancing the field of X-ray tomography.…”