Dr. Jay Rubinstein Profile

Dr. Jay Rubinstein

at Univ. of Washington

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Publications (1)

Proceedings Article | 14 October 2022 Presentation + Paper

Motion correction for robot-based x-ray photon-counting CT at ultrahigh resolution

Mengzhou Li, Jana Bohacova, Josef Uher, Wenxiang Cong, Jay Rubinstein, Ge Wang

Proceedings Volume 12242, 122420Y (2022) https://doi.org/10.1117/12.2635263

KEYWORDS: Sensors, Robotics, X-ray computed tomography, X-rays, Scanners, Calibration, Image resolution, Imaging systems, X-ray sources, Reconstruction algorithms

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Ultrahigh-resolution CT is highly desirable in many clinical applications, such as cochlear implantation and coronary stenosis assessment. Despite the wide accessibility to micro-CT for preclinical research, there are no such micro-CT scanners for clinical use, due to several issues including high radiation dose, patient movement, limited X-ray source power and long image time. To meet the challenge, here we design a robotic-arm-based clinical photon-counting micro-CT system in the interior tomography mode for temporal bone imaging at a 50 µm resolution. We adopt twin collaborative robotic arms. These robotic arms can be operated near humans without danger of injury due to collision. One robot steers the source, the other the detector. The six-axis robot pair defines a volume of interest (VOI) more flexibly than the traditional rotation gantries and C-arm systems. The robotic system allows arbitrary imaging geometry. The required spatial resolution is on the same order of the robot mechanical precision, therefore the geometric errors due to robotic coordination together with patient motion, and system misalignment must be addressed. In this paper, we propose a locally linear embedding-based motion correction method to solve this problem to correct all these geometric errors in an unified framework with a nine-degree-of-freedom model. Different from the conventional motion correction methods that rely on gradient-based parametric optimization, our method utilizes a parametric searching mechanism through iterative reconstruction and without assuming any smoothness of the errors as a function of the view angle. The effectiveness of our method is first demonstrated with numerical simulation and further verified on a set of limited-angle cone-beam scans of a sacrificed mouse using our robotic-arm-based imaging system. Sharper images with clearer anatomical details in the absence of misalignment artifacts are obtained after our data-driven geometric calibration, suggesting a great potential of our method in biomedical and industrial applications.

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