An evaluation of organ dose modulation on a GE optima CT660-computed tomography scanner.
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ABSTRACT:
SUBMITTER: Dixon MT
PROVIDER: S-EPMC5690941 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The results of a long-term, comprehensive CT quality control (QC) program were analyzed to investigate differences in failure rates based on QC test, scanner utilization pattern, and number of channels, as well as explore issues regarding testing frequency.CT QC data were collected over a 4-yr period for 26 CT scanners representing two different vendors and using three different QC programs culminating in over 100 scanner-years of QC data. QC tests analyzed included water tests [mean CT number, standard deviation, and uniformity], linearity tests [air, water, and acrylic], and artifact analysis [water phantom and large phantom]. The data were organized based on scanner use, number of channels, scanner modality, and QC test. Logistic regression model analysis with generalized estimating equation method was used to estimate failure rates for each group.A significant difference between failure rates with respect to QC test was found (p-value = 0.02). Large phantom artifacts, standard deviation of water, and water phantom artifacts had the three highest failure rates. No significant difference was found between failure rates organized by scanner use, scanner modality, or number of channels.Standard deviation of water is the most important quantitative value to collect as part of a daily QC program. Uniformity and linearity tests have relatively low failure rates and, therefore, may not require daily verification. While its failure rates were moderate, daily artifact analysis is suggested due to its potentially high impact on clinical image quality. Weekly or monthly large phantom artifact analysis is encouraged for those sites possessing an appropriate phantom.
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Project description:Background and purposeComputed tomography (CT) scanning is the basis for radiation treatment planning, but the 50-cm standard scanning field of view (sFOV) may be too small for imaging larger patients. We evaluated the 65-cm high-definition (HD) FOV of a large-bore CT scanner for CT number accuracy, geometric distortion, image quality degradation, and dosimetric accuracy of photon treatment plans.Materials and methodsCT number accuracy was tested by placing two 16-cm acrylic phantoms on either side of a 40-cm phantom to simulate a large patient extending beyond the 50-cm-diameter standard scanning FOV. Dosimetric accuracy was tested using anthropomorphic pelvis and thorax phantoms, with additional acrylic body parts on either side of the phantoms. Two volumetric modulated arc therapy beams (a 15-MV and a 6-MV) were used to cover the planning target volumes. Two-dimensional dose distributions were evaluated with GAFChromic film and point dose accuracy was checked with multiple thermoluminescent dosimeter (TLD) capsules placed in the phantoms. Image quality was tested by placing an American College of Radiology accreditation phantom inside the 40-cm phantom.ResultsThe HD FOV showed substantial changes in CT numbers, with differences of 314 HU-725 HU at different density levels. The volume of the body parts extending into the HD FOV was distorted. However, TLD-reported doses for all PTVs agreed within ± 3%. Dose agreement in organs at risk were within the passing criteria, and the gamma index pass rate was >97%. Image quality was degraded.ConclusionsThe HD FOV option is adequate for RT simulation and met accreditation standards, although care should be taken during contouring because of reduced image quality.
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Project description:ImportanceLung cancer screening has been widely implemented in Europe and the US. However, there is little evidence on participation and diagnostic yields in population-based lung cancer screening in China.ObjectiveTo assess the participation rate and detection rate of lung cancer in a population-based screening program and the factors associated with participation.Design, setting, and participantsThis cross-sectional study used data from the Cancer Screening Program in Urban China from October 2013 to October 2019, with follow-up until March 10, 2020. The program is conducted at centers in 8 cities in Henan Province, China. Eligible participants were aged 40 to 74 and were evaluated for a high risk for lung cancer using an established risk score system.Main outcomes and measuresOverall and group-specific participation rates by common factors, such as age, sex, and educational level, were calculated. Differences in participation rates between those groups were compared. The diagnostic yield of both screening and nonscreening groups was calculated.ResultsThe study recruited 282 377 eligible participants and included 55 428 with high risk for lung cancer; the mean (SD) age was 55.3 (8.1) years, and 34 966 participants (63.1%) were men. A total of 22 260 participants underwent LDCT (participation rate, 40.16%; 95% CI, 39.82%-40.50%). The multivariable logistic regression model showed that female sex (odds ratio [OR], 1.64; 95% CI, 1.52-1.78), former smoking (OR, 1.26; 95% CI, 1.13-1.41), lack of physical activity (OR, 1.19; 95% CI, 1.14-1.24), family history of lung cancer (OR, 1.73; 95% CI, 1.66-1.79), and 7 other factors were associated with increased participation of LDCT screening. Overall, at 6-year follow-up, 78 participants in the screening group (0.35%; 95% CI, 0.29%-0.42%) and 125 in the nonscreening group (0.38%; 95% CI, 0.33%-0.44%) had lung cancer detected, which resulted in an odds ratio of 0.93 (95% CI, 0.70-1.23; P = .61).Conclusions and relevanceThe low participations rate in the program studied suggests that an improved strategy is needed. These findings may provide useful information for designing effective population-based lung cancer screening strategies in the future.
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Project description:With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent automated analysis of morphology and pathology. The first "Large Scale Vertebrae Segmentation Challenge" (VerSe 2019) showed that these perform well on normal anatomy, but fail in variants not frequently present in the training dataset. Building on that experience, we report on the largely increased VerSe 2020 dataset and results from the second iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020 comprises annotated spine computed tomography (CT) images from 300 subjects with 4142 fully visualized and annotated vertebrae, collected across multiple centres from four different scanner manufacturers, enriched with cases that exhibit anatomical variants such as enumeration abnormalities (n = 77) and transitional vertebrae (n = 161). Metadata includes vertebral labelling information, voxel-level segmentation masks obtained with a human-machine hybrid algorithm and anatomical ratings, to enable the development and benchmarking of robust and accurate segmentation algorithms.
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Project description:PurposeThe image noise and image quality of a prototype ultra-high-resolution computed tomography (U-HRCT) scanner was evaluated and compared with those of conventional high-resolution CT (C-HRCT) scanners.Materials and methodsThis study was approved by the institutional review board. A U-HRCT scanner prototype with 0.25 mm x 4 rows and operating at 120 mAs was used. The C-HRCT images were obtained using a 0.5 mm x 16 or 0.5 mm x 64 detector-row CT scanner operating at 150 mAs. Images from both scanners were reconstructed at 0.1-mm intervals; the slice thickness was 0.25 mm for the U-HRCT scanner and 0.5 mm for the C-HRCT scanners. For both scanners, the display field of view was 80 mm. The image noise of each scanner was evaluated using a phantom. U-HRCT and C-HRCT images of 53 images selected from 37 lung nodules were then observed and graded using a 5-point score by 10 board-certified thoracic radiologists. The images were presented to the observers randomly and in a blinded manner.ResultsThe image noise for U-HRCT (100.87 ± 0.51 Hounsfield units [HU]) was greater than that for C-HRCT (40.41 ± 0.52 HU; P < .0001). The image quality of U-HRCT was graded as superior to that of C-HRCT (P < .0001) for all of the following parameters that were examined: margins of subsolid and solid nodules, edges of solid components and pulmonary vessels in subsolid nodules, air bronchograms, pleural indentations, margins of pulmonary vessels, edges of bronchi, and interlobar fissures.ConclusionDespite a larger image noise, the prototype U-HRCT scanner had a significantly better image quality than the C-HRCT scanners.
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Project description:Quantitative computed tomography (CT) texture analyses for images with and without filtration are gaining attention to capture the heterogeneity of tumors. The aim of this study was to investigate how quantitative texture parameters using image filtering vary among different computed tomography (CT) scanners using a phantom developed for radiomics studies.A phantom, consisting of 10 different cartridges with various textures, was scanned under 6 different scanning protocols using four CT scanners from four different vendors. CT texture analyses were performed for both unfiltered images and filtered images (using a Laplacian of Gaussian spatial band-pass filter) featuring fine, medium, and coarse textures. Forty-five regions of interest were placed for each cartridge (x) in a specific scan image set (y), and the average of the texture values (T(x,y)) was calculated. The interquartile range (IQR) of T(x,y) among the 6 scans was calculated for a specific cartridge (IQR(x)), while the IQR of T(x,y) among the 10 cartridges was calculated for a specific scan (IQR(y)), and the median IQR(y) was then calculated for the 6 scans (as the control IQR, IQRc). The median of their quotient (IQR(x)/IQRc) among the 10 cartridges was defined as the variability index (VI).The VI was relatively small for the mean in unfiltered images (0.011) and for standard deviation (0.020-0.044) and entropy (0.040-0.044) in filtered images. Skewness and kurtosis in filtered images featuring medium and coarse textures were relatively variable across different CT scanners, with VIs of 0.638-0.692 and 0.430-0.437, respectively.Various quantitative CT texture parameters are robust and variable among different scanners, and the behavior of these parameters should be taken into consideration.
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