Task-Specific Trajectory Optimisation for Twin-Robotic X-Ray Tomography

Herl, Gabriel; Hiller, Jochen; Thies, Mareike; Zaech, Jan-Nico; Unberath, Mathias; Maier, Andreas

doi:10.1109/tci.2021.3102824

Cited by 18 publications

(18 citation statements)

References 18 publications

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“…However, the same formulations can be used to represent different geometries as well. Optimization algorithms could be used to find optimal geometries similarly to how robot arm CT geometries are optimized [11]. Apart from the geometry the trade-off between other parameters could be further investigated and optimized such as: the number of overlapping objects, the number of projections, detector size, tube current, tube voltage and exposure time.…”

Section: Discussionmentioning

confidence: 99%

TOP-CT: Trajectory With Overlapping Projections X-Ray Computed Tomography

Schut

Batenburg

Liere

et al. 2022

IEEE Trans. Comput. Imaging

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Section: Discussionmentioning

confidence: 99%

TOP-CT: Trajectory With Overlapping Projections X-Ray Computed Tomography

Schut

Batenburg

Liere

et al. 2022

IEEE Trans. Comput. Imaging

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“…14,15 The widely used robotic C-arm CBCT can provide flexible trajectories according to actual needs. 13 Many studies on robotic C-arm CBCT have shown that task-specific trajectories obtained through trajectory optimization can reduce quantum noise 16 and metal artifacts, 17 improve image quality 18,19 and detectability of implants for a given radiation dose, 20 and avoid collision under strong kinematic constraints. 21 Micro CT is a common CBCT, which is widely used in industrial testing and preclinical experiments.…”

Section: Introductionmentioning

confidence: 99%

A nonconvex model‐based combined geometric calibration scheme for micro cone‐beam CT with irregular trajectories

Xue

You

et al. 2023

Medical Physics

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Background Many dedicated cone‐beam CT (CBCT) systems have irregular scanning trajectories. Compared with the standard CBCT calibration, accurate calibration for CBCT systems with irregular trajectories is a more complex task, since the geometric parameters for each scanning view are variable. Most of the existing calibration methods assume that the intrinsic geometric relationship of the fiducials in the phantom is precisely known, and rarely delve deeper into the issue of whether the phantom accuracy is adapted to the calibration model. Purpose A high‐precision phantom and a highly robust calibration model are interdependent and mutually supportive, and they are both important for calibration accuracy, especially for the high‐resolution CBCT. Therefore, we propose a calibration scheme that considers both accurate phantom measurement and robust geometric calibration. Methods Our proposed scheme consists of two parts: (1) introducing a measurement model to acquire the accurate intrinsic geometric relationship of the fiducials in the phantom; (2) developing a highly noise‐robust nonconvex model‐based calibration method. The measurement model in the first part is achieved by extending our previous high‐precision geometric calibration model suitable for CBCT with circular trajectories. In the second part, a novel iterative method with optimization constraints based on a back‐projection model is developed to solve the geometric parameters of each view. Results The simulations and real experiments show that the measurement errors of the fiducial ball bearings (BBs) are within the subpixel level. With the help of the geometric relationship of the BBs obtained by our measurement method, the classic calibration method can achieve good calibration based on far fewer BBs. All metrics obtained in simulated experiments as well as in real experiments on Micro CT systems with resolutions of 9 and 4.5 μm show that the proposed calibration method has higher calibration accuracy than the competing classic method. It is particularly worth noting that although our measurement model proves to be very accurate, the classic calibration method based on this measurement model can only achieve good calibration results when the resolution of the measurement system is close to that of the system to be calibrated, but our calibration scheme enables high‐accuracy calibration even when the resolution of the system to be calibrated is twice that of the measurement system. Conclusions The proposed combined geometrical calibration scheme does not rely on a phantom with an intricate pattern of fiducials, so it is applicable in Micro CT with high resolution. The two parts of the scheme, the “measurement model” and the “calibration model,” prove to be of high accuracy. The combination of these two models can effectively improve the calibration accuracy, especially in some extreme cases.

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“…The concept of using a priori object information to improve the quality of XCT reconstructions is not new. A review of the literature shows that a number of methods for optimizing the XCT scan strategy based on a priori object information have been proposed [5][6][7][8][9][10][11][12][13]. These approaches generally aim to select an optimal object scan orientation, or source-detector scan trajectory; this is achieved by simulating projections for all possible object orientations, and then selecting the object orientation or source-detector trajectory that maximizes some quality metric.…”

Section: Introductionmentioning

confidence: 99%

“…Herl et al [9] developed a method to select projection angles to reconstruct a region of interest of an object using a dual robot XCT system; the completeness of Radon sampling and material penetration lengths were used to select projections from a set of candidate projections. Herl et al further developed their method in [10] by investigating the use of an alternative quality metric based on the modulation transfer function and noise power spectrum of the reconstruction, which has also been utilised in recent work by Bauer et al [12].…”

Section: Introductionmentioning

confidence: 99%

IntelliScan: Improving the quality of x-ray computed tomography surface data through intelligent selection of projection angles

Lifton¹,

Poon

2023

XST

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X-ray computed tomography (XCT) enables the dimensional measurement and inspection of highly geometrically complex engineering components that are unmeasurable using optical and tactile instruments. Conventional XCT scans use a circular scan trajectory where X-ray projections are acquired with a uniform angular spacing; this approach treats all projections as being of equal importance, in practice, some projections contain more object information than others. In this work we capitalize on this concept by intelligently selecting projections with a view to improve the quality of surface models extracted from an XCT data-set. Our approach relies on using a priori object information to select X-ray projections in which the surfaces of the object are aligned with a ray-path, thus ensuring the surface of the object is fully sampled. Results are presented showing that the proposed method is able to reduce CAD comparison errors by 16%, reduce surface form error by 3%, and improve edge contrast by 14% for a machined aluminium component.

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Task-Specific Trajectory Optimisation for Twin-Robotic X-Ray Tomography

Cited by 18 publications

References 18 publications

TOP-CT: Trajectory With Overlapping Projections X-Ray Computed Tomography

TOP-CT: Trajectory With Overlapping Projections X-Ray Computed Tomography

A nonconvex model‐based combined geometric calibration scheme for micro cone‐beam CT with irregular trajectories

IntelliScan: Improving the quality of x-ray computed tomography surface data through intelligent selection of projection angles

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