Online geometry calibration for retrofit computed tomography from a mouse rotation system and a small‐animal imager. Issue 1 (12th September 2022)

• Record Type: Journal Article
• Authors: Zhou, Huanyi; Reeves, Stanley; Chou, Cheng‐Ying; Brannen, Andrew; Panizzi, Peter
• Is Part Of: Medical physics. Volume 50:Issue 1(2023)
• Page Start: 192
• Page End: 208
• Publication Date: 2022-09-12
• Subjects: CT preprocessing -- online geometry calibration -- preclinical CBCT; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15953
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: 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.