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. 2020 Nov;21(11):215-225.
doi: 10.1002/acm2.13062. Epub 2020 Oct 19.

CBCT image quality QA: Establishing a quantitative program

Affiliations

CBCT image quality QA: Establishing a quantitative program

Sameer Taneja et al. J Appl Clin Med Phys. 2020 Nov.

Abstract

Purpose: Routine quality assurance (QA) of cone-beam computed tomography (CBCT) scans used for image-guided radiotherapy is prescribed by the American Association of Physicists in Medicine Task Group (TG)-142 report. For CBCT image quality, TG-142 recommends using clinically established baseline values as QA tolerances. This work examined how image quality parameters vary both across machines of the same model and across different CBCT techniques. Additionally, this work investigated how image quality values are affected by imager recalibration and repeated exposures during routine QA.

Methods: Cone-beam computed tomography scans of the Catphan 604 phantom were taken on four TrueBeam® and one Edge™ linear accelerator using four manufacturer-provided techniques. TG-142 image quality parameters were calculated for each CBCT scan using SunCHECK Machine™. The variability of each parameter with machine and technique was evaluated using a two-way ANOVA test on a dataset consisting of 200 CBCT scans. The impact of imager calibration on image quality parameters was examined for a subset of three machines using an unpaired Student's t-test. The effect of artifacts appearing on CBCTs taken in rapid succession was characterized and an approach to reduce their appearance was evaluated. Additionally, a set of baselines and tolerances for all image quality metrics was presented.

Results: All imaging parameters except geometric distortion varied with technique (P < 0.05) and all imaging parameters except slice thickness varied with machine (P < 0.05). Imager calibration can change the expected value of all imaging parameters, though it does not consistently do so. While changes are statistically significant, they may not be clinically significant. Finally, rapid acquisition of CBCT scans can introduce image artifacts that degrade CBCT uniformity.

Conclusions: This work characterized the variability of acquired CBCT data across machines and CBCT techniques along with the impact of imager calibration and rapid CBCT acquisition on image quality.

Keywords: cone-beam computed tomography; image quality; institutional baselines; linear accelerator quality assurance.

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Conflict of interest statement

David L. Barbee reports Sun Nuclear Corporation paid for conference travel to speak. None of the other authors has any conflict of interest to disclose.

Figures

Fig. 1
Fig. 1
Registered cone‐beam computed tomography scan of the Catphan® 604 in SunCHECK Machine. Catphan modules shown in (a), (b), and (c) are used for analysis corresponding to parameters presented in Table 3.
Fig. 2
Fig. 2
(a) Transverse and (b) sagittal planes of the module used for uniformity analysis of a cone‐beam computed tomography acquired using the Head technique and with the presence of a central dark artifact. (c) An HU line profile across the center of the transverse plane.
Fig. 3
Fig. 3
Institutional data (N = 200) for all image quality parameters separated by technique and machine and used for the two‐way ANOVA test. Error bars represent a 95% confidence interval.
Fig. 4
Fig. 4
The maximum change in the average value of each HU plug caused by recalibration of the cone‐beam computed tomography imager plotted along with the range of expected values after calibration seen across all machines. Error bars are the largest, single machine standard deviation computed from post‐calibration data intra‐machine for each parameter.
Fig. 5
Fig. 5
Catphan® uniformity module for cone‐beam computed tomographies (CBCTs) acquired in rapid succession and post 10‐min interval. The calculated uniformity value is presented for each CBCT.

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