Dataset Information

CBCT image quality QA: Establishing a quantitative program.

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.

SUBMITTER: Taneja S

PROVIDER: S-EPMC7701111 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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CBCT image quality QA: Establishing a quantitative program.

Taneja Sameer S Barbee David L DL Rea Anthony J AJ Malin Martha M

Journal of applied clinical medical physics 20201019 11

<h4>Purpose</h4>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 ...[more]

PMID: 33078562

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Project description:The image quality (IQ) of imaging systems must be sufficiently high for image-guided radiation therapy (IGRT). Hence, users should implement a quality assurance program to maintain IQ. In our routine IQ tests of the kV cone-beam CT system (Elekta XVI), image noise was quantified by noise standard deviation (NSD), which was the standard deviation of CT numbers measured in a small area in an image of an IQ test phantom (Catphan), and the high spatial resolution (HSR) was evaluated by the number of line-pairs (LPN) visually recognizable in the image. We also measured the image uniformity, the low contrast resolution, and the distances of two points for geometrical accuracy. For this study, we did an additional evaluation of the XVI data for 12 monthly IQ tests by using noise power spectrum (NPS) for noise, modulation transfer function (MTF) for HSR, and CT number-to-density relationship. NPS was obtained by applying Fourier analysis in a small area on the uniformity test section of Catphan. The MTF analysis was performed by applying the Droege-Morin (D-M) method to the line-pair bar regions in the phantom. The CT number-to-density relationship was obtained for insert materials in the low-contrast test section of the phantom. All the quantities showed a noticeable change over the one-year period. Especially the noise level improved significantly after a repair of the imager. NPS was more sensitive to the IQ change than NSD. MTF could provide more quantitative and objective evaluation of HSR. The CT number was very different from the expected CT number, but the CT number-to-density curves were constant within 5% except for two months. Since the D-M method is easy to implement, we recommend using MTF instead of LPN even for routine QA. The IQ of the imaging systems was constantly changing; hence, IQ tests should be periodically performed. Additionally, we found the importance of IQ tests after every service work, including detector calibration as well as preventive maintenance.

Project description:Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.

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CBCT image quality QA: Establishing a quantitative program.

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CBCT image quality QA: Establishing a quantitative program.

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