Technical Note: Assessing the performance of monthly CBCT image quality QA

Comparison of visual and semi-automated kilovoltage cone beam CT image QA analysis

Established kilovoltage cone-beam computed tomography (kV-CBCT) image quality assurance (QA) guidelines often rely on recommendations provided by the American Association of Physicists in Medicine (AAPM) task group (TG) reports with metrics that use visual analysis. This can result in measurement variations by different users, especially in visually subjective analyzes such as low contrast resolution. Consequently, there is a growing interest in more automated means of image QA analysis that can offer increased consistency, accuracy, and convenience. This work compares visual QA to semi-automated software QA analysis to establish the performance and viability of a semi-automated method. In this study, a commercial product (RIT Radia. Radiological Imaging Technology, Colorado Springs, CO) was used to evaluate 68 months of kV-CBCT images of a Catphan® 504 phantom obtained from a Varian TrueBeam® linear accelerator. Six key metrics were examined: high contrast resolution, low contrast resolution, Hounsfield unit constancy, uniformity and noise, and spatial linearity. The results of this method were then compared to those recorded visually using Bland-Altman, and/or paired sample t-test. Comparison of all modules showed a non-random, statistically significant difference between visual and semi-automated methods except for LDPE and Teflon in the Hounsfield unit constancy analysis, which falls outside the paired sample t-test's 5% significance level. A small high contrast resolution bias indicates the two analysis methods are largely equivalent, while a large low contrast resolution bias indicates greater semi-automated target detection. Wide limits of agreement in the uniformity module suggests variability due to multiple visual observers. Spatial linearity results measured differences of less than 0.17%. Semi-automated QA analysis offered greater stability over visual analysis. Additionally, semi-automated QA results satisfied or exceeded visual QA passing criteria and allowed for fast and consistent image quality analysis.

A step closer to automation: kilovoltage and Megavoltage Planar Imaging Quality Assurance, baseline, tolerance and action levels definition and exploration

PURPOSE

To establish automated quality assurance (QA) procedures for the kilovoltage (kV) and the Megavoltage (MV) imagers of two linear accelerators (LINACS) using a commercial software.

METHODS

SNC Machine™ phantoms and software were used and the baseline values, tolerance and action levels for various image quality parameters were defined. Scaling, spatial resolution, contrast, uniformity and noise were considered, explored and evaluated utilizing the appropriate phantoms and the accompanying software. kV and MV planar radiographic images, for 6MV and 10MV beams were obtained for each LINAC. For both kV and MV QA tasks, the baseline values for spatial resolution, contrast, uniformity and noise were defined.

RESULTS

Subsequent measurements performed were highly reproducible and within tolerance and action levels, while noise showed variations. The calculated tolerance and action levels for noise were looser compared to the other image quality metrics.

CONCLUSIONS

An automated QA workflow of the kV and MV planar radiographic mode of LINAC imagers' was established and appears to be time effective.

Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging

OBJECTIVES

Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images.

METHODS

A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used. A solid tumor tissue removed from a male BALB/c mouse was included. We the placed phantom sets on the CT scanning table and repeated 20 acquisitions with identical imaging settings. Regions of interest were delineated for feature extraction. Statistical quantities-average, standard deviation, and percentage uncertainty-were calculated from these 20 repeated scans. Percentage uncertainty was used to measure and quantify feature stability against quantum noise. Twelve radiomics features were measured. Random noise was added to study the robustness of machine learning classifiers against feature uncertainty.

RESULTS

We found the ranges of percentage uncertainties from homogeneous soft tissue phantoms, homogeneous bone phantoms, and solid tumor tissue to be 0.01-2138%, 0.02-15%, and 0.18-16%, respectively. Overall, it was found that the CT features ShortRunHighGrayLevelEmpha (SRHGE) (0.01-0.18%), ShortRunLowGrayLevelEmpha (SRLGE) (0.01-0.41%), LowGrayLevelRunEmpha (LGRE) (0.01-0.39%), and LongRunLowGrayLevelEmpha (LRLGE) (0.02-0.66%) were the most stable features against the inherent quantum noise. The most unstable features were cluster shade (1-2138%) and max probability (1-16%). The impact of random noise to the prediction accuracy by different machine learning classifiers was found to be between 0 and 12%.

CONCLUSIONS

Twelve features were used for uncertainty measurements. The upper and lower bounds of percentage uncertainties were determined. The quantum noise effect on machine learning classifiers is model dependent.

KEY POINTS

• Quantum noise is a random process and is intrinsic to X-ray-based imaging systems. This inherent quantum noise creates unpredictable fluctuations in the gray-level intensities of image pixels. Extra cautions and further validations are strongly recommended when unstable radiomics features are selected by a predictive model for disease classification or treatment outcome prognosis. • We addressed and used the statistical quantity of percentage uncertainty to measure the uncertainty of radiomics features against the inherent quantum noise in computed tomography (CT) images. • A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used in the stability measurement. A solid tumor tissue removed from a male BALB/c mouse was included in the heterogeneous sample.

CBCT image quality QA: Establishing a quantitative program

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.