Geometry calibration method for a cone‐beam <scp>CT</scp> system

Yang, Honggang; Kang, Kejun; Xing, Yuxiang

doi:10.1002/mp.12163

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…26,27 This contrasts with CT images, which are calibrated to a phantom. 28,29 Thus, measurements between modalities can vary due to geometric distortion. Although normative distances on this study were acquired by CT myelography rather than fluoroscopy, the flare point technique itself does not depend on transposition of distances from one technique to another.…”

Section: Discussionmentioning

confidence: 99%

C1 Posterior Arch Flare Point: A Useful Landmark for Fluoroscopically Guided C1–2 Puncture

Peckham

Shah

Tsai

et al. 2018

AJNR Am J Neuroradiol

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Section: Discussionmentioning

confidence: 99%

C1 Posterior Arch Flare Point: A Useful Landmark for Fluoroscopically Guided C1–2 Puncture

Peckham

Shah

Tsai

et al. 2018

AJNR Am J Neuroradiol

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“…Traditionally, parameters are considered independent of each other 14 . We fix the geometry error in the order of

(\eta , n, \theta , m, \varphi )

.…”

Section: Methodsmentioning

confidence: 99%

“…used 24 steel beads as a phantom to get a set of projections for parameter estimation. Yang et al 14 . reduced the phantom to 12 steel beads on two planes in a cylinder phantom for error assessment.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Cho et al 13 used 24 steel beads as a phantom to get a set of projections for parameter estimation. Yang et al 14 reduced the phantom to 12 steel beads on two planes in a cylinder phantom for error assessment. Sun et al 15 combined prior relative position information with only four beads in a single projection to calculate the results.…”

Section: Introductionmentioning

confidence: 99%

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Online geometry calibration for retrofit computed tomography from a mouse rotation system and a small‐animal imager

et al. 2022

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Background Computed tomography (CT) generates a three‐dimensional rendering that can be used to interrogate a given region or desired structure from any orientation. However, in preclinical research, its deployment remains limited due to relatively high upfront costs. Existing integrated imaging systems that provide merged planar X‐ray also dwarfs CT popularity in small laboratories due to their added versatility. Purpose In this paper, we sought to generate CT‐like data using an existing small‐animal X‐ray imager with a specialized specimen rotation system, or MiSpinner. This setup conforms to the cone‐beam CT (CBCT) geometry, which demands high spatial calibration accuracy. Therefore, a simple but robust geometry calibration algorithm is necessary to ensure that the entire imaging system works properly and accurately. Methods Because the rotation system is not permanently affixed, we propose a structure tensor‐based two‐step online (ST‐TSO) geometry calibration algorithm. Specifically, two datasets are needed, namely, calibration and actual measurements. A calibration measurement detects the background of the system forward X‐ray projections. A study on the background image reveals the characteristics of the X‐ray photon distribution, and thus, provides a reliable estimate of the imaging geometry origin. Actual measurements consisted of an X‐ray of the intended object, including possible geometry errors. A comprehensive image processing technique helps to detect spatial misalignment information. Accordingly, the first processing step employs a modified projection matrix‐based calibration algorithm to estimate the relevant geometric parameters. Predicted parameters are then fine‐tuned in a second processing step by an iterative strategy based on the symmetry property of the sum of projections. Virtual projections calculated from the parameters after two‐step processing compensate for the scanning errors and are used for CT reconstruction. Experiments on phantom and mouse imaging data were performed to validate the calibration algorithm. Results Once system correction was conducted, CBCT of a CT bar phantom and a cohort of euthanized mice were analyzed. No obvious structure error or spatial artifacts were observed, validating the accuracy of the proposed geometry calibration method. Digital phantom simulation indicated that compared with the preset spatial values, errors in the final estimated parameters could be reduced to 0.05° difference in dominant angle and 0.5‐pixel difference in dominant axis bias. The in‐plane resolution view of the CT‐bar phantom revealed that the resolution approaches 150 μ$\umu$m. Conclusions A constrained two‐step online geometry calibration algorithm has been developed to calibrate an integrated X‐ray imaging system, defined by a first‐step analytical estimation and a second‐step iterative fine‐tuning. Test results have validated its accuracy in system correction, thus demonstrating the potential of the described system to be modified and adapted for preclinical research.

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“…A rudimentary example of how to measure the SDD is presented in [1], where two radiographs of a reference object are imaged at different known positions and compared to yield a distance estimate. In addition to this example, works by Noo et al [11], von Smekal et al [12], Sawall et al [13], Zhao et al [14], Zhang et al [15], Yang et al [16], Illemann et al [17], Ferruci et al [19], and Bircher et al [18] are only some of many possible approaches to CT scanner calibration. These go beyond measuring only the SOD and SDD, as they attempt to describe a calibrated scanner's geometry completely.…”

Section: Introductionmentioning

confidence: 99%

Voxel Size Calibration for High-resolution CT

Zemek¹,

Blažek²,

Šrámek³

et al. 2020

eJNDT

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In cone-beam X-ray computed tomography (CT), distances between the source, object, and detector influence the visual fidelity and voxel size of a reconstructed volume. Calibration using reference objects is an appropriate tool for preventing errors in the estimates of these distances. There is, however, a lack of such objects for high-resolution systems with a small field of view (FoV). In this work, we propose a method to measure the distances mentioned above, improving the determination of voxel size. We use a custom reference object suitable for a FoV of around one millimeter. Many approaches have been developed for this calibration task and discussed in the literature, but none apply to CT scanners with a small FoV and a cone-beam magnification close to one. The proposed method thus aims to provide a calibration procedure for such devices. A Rigaku Nano3DX CT scanner has been calibrated through this method and used for practical validation of the method's accuracy. Results have shown that this approach allows for accurate calibration, which leads to improvements in reconstruction quality and accuracy of voxel size determination.

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Geometry calibration method for a cone‐beam CT system

Cited by 16 publications

References 17 publications

C1 Posterior Arch Flare Point: A Useful Landmark for Fluoroscopically Guided C1–2 Puncture

C1 Posterior Arch Flare Point: A Useful Landmark for Fluoroscopically Guided C1–2 Puncture

Online geometry calibration for retrofit computed tomography from a mouse rotation system and a small‐animal imager

Voxel Size Calibration for High-resolution CT

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