Learning-based Trajectory Optimization for a Twin Robotic CT System

Schneider LS, Thies M, Schielein R, Syben C, Unberath M, Maier A (2023)

Publication Type: Conference contribution, Original article

Publication year: 2023

Original Authors: Linda-Sophie Schneider, , Mareike Thies, Richard Schielein, Christopher Syben, Mathias Unberath, Andreas Maier, , , , ,

Series: iCT 2023 | Vol. 28(3)

Book Volume: 28

Conference Proceedings Title: NDT.net Issue: 2023-03

Event location: Fürth DE

Issue: 3

DOI: 10.58286/27748

Abstract

Non-circular twin robot computed tomography (CT) systems provide a high degree of flexibility in positioning the X-ray source and detector. This makes the design of optimal CT trajectories particularly important, as it can potentially allow for a reduction in the number of projections required for image acquisition. Therefore, the optimization of CT trajectories is a key area of research in this field. This paper presents a method for optimising the trajectory of a non-circular twin robot computed tomography system, using a so-called detectability index to guide the optimization process. The proposed method involves simulating possible X-ray projections that are reachable by the robotic CT system and using a regressive ResNet-34 to predict the detectability index of each projection. An integer optimization problem is then used to generate an optimised CT trajectory based on this prediction, with the haversine distance between projections serving as an additional constraint. The performance of the ResNet-34 is evaluated on unseen projections. It effectively predicted the detectability index for a spherical sampling space, allowing for the optimization of arbitrary CT trajectories. In addition, the method was able to identify valuable projections according to the detectability index, enabling the reconstruction of a test object with only 30 out of 800 of the previously acquired projections without significant loss of information in the reconstruction domain.

Authors with CRIS profile

Linda-Sophie Schneider Lehrstuhl für Informatik 5 (Mustererkennung) Mareike Thies Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Fraunhofer Development Center X-ray Technology (EZRT) DE Germany (DE) Johns Hopkins University (JHU) US United States (USA) (US)

How to cite

APA:

Schneider, L.-S., Thies, M., Schielein, R., Syben, C., Unberath, M., & Maier, A. (2023). Learning-based Trajectory Optimization for a Twin Robotic CT System. In NDT.net Issue: 2023-03. Fürth, DE.

MLA:

Schneider, Linda-Sophie, et al. "Learning-based Trajectory Optimization for a Twin Robotic CT System." Proceedings of the 12th Conference on Industrial Computed Tomography (iCT) 2023, Fürth 2023.

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