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:The use of miniature pigs in non-clinical studies for medical drugs or devices has gradually been increasing in recent years. It is anticipated that the use of juvenile miniature pigs in laboratory practice will also increase. Therefore, it is important to investigate various parameters of juvenile miniature pigs. The body surface area (BSA) of an organism is one of the important parameters for evaluating physiological functions. In drug development, normalization by BSA is an appropriate method for extrapolating doses between species. The BSA of animals has generally been estimated by multiplying the k value by 2/3 of the power of the body weight (BW) (Meeh's formula). To our knowledge, the BSA of juvenile miniature pigs has not as yet been reported. In this study, we measured the BSA of 13 miniature pigs less than 1 month old, using a computed tomography scanner and 3-dimensional analysis software. The measurement results showed the BSAs of these 13 juvenile miniature pigs to be in the range of 386 to 1,672 cm2(working BW range: 278 to 3,200 g). After BSA determination, the k values were calculated from the BSA and the BW. The mean calculated k value was 8.58. We advocate using Meeh's formula, as follows, for estimating the BSA of juvenile miniature pigs less than 1 month old (before weaning): BSA (cm2)=8.58 × BW (g)2/3.
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Project description:Screening for lung cancer has the potential to reduce mortality, but in addition to detecting aggressive tumors, screening will also detect indolent tumors that otherwise may not cause clinical symptoms. These overdiagnosis cases represent an important potential harm of screening because they incur additional cost, anxiety, and morbidity associated with cancer treatment.To estimate overdiagnosis in the National Lung Screening Trial (NLST).We used data from the NLST, a randomized trial comparing screening using low-dose computed tomography (LDCT) vs chest radiography (CXR) among 53?452 persons at high risk for lung cancer observed for 6.4 years, to estimate the excess number of lung cancers in the LDCT arm of the NLST compared with the CXR arm.We calculated 2 measures of overdiagnosis: the probability that a lung cancer detected by screening with LDCT is an overdiagnosis (PS), defined as the excess lung cancers detected by LDCT divided by all lung cancers detected by screening in the LDCT arm; and the number of cases that were considered overdiagnosis relative to the number of persons needed to screen to prevent 1 death from lung cancer.During follow-up, 1089 lung cancers were reported in the LDCT arm and 969 in the CXR arm of the NLST. The probability is 18.5% (95% CI, 5.4%-30.6%) that any lung cancer detected by screening with LDCT was an overdiagnosis, 22.5% (95% CI, 9.7%-34.3%) that a non-small cell lung cancer detected by LDCT was an overdiagnosis, and 78.9% (95% CI, 62.2%-93.5%) that a bronchioalveolar lung cancer detected by LDCT was an overdiagnosis. The number of cases of overdiagnosis found among the 320 participants who would need to be screened in the NLST to prevent 1 death from lung cancer was 1.38.More than 18% of all lung cancers detected by LDCT in the NLST seem to be indolent, and overdiagnosis should be considered when describing the risks of LDCT screening for lung cancer.
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Project description:Different cone beam computed tomography (CBCT) protocols have shown promising results for imaging furcation defects. This study evaluates the suitability of low-dose (LD)-CBCT for this purpose. Fifty-nine furcation defects of nine upper and 16 lower molars in six human cadavers were measured by a high-dose (HD)-CBCT protocol, a LD-CBCT protocol, and a surgical protocol. HD-CBCT and LD-CBCT measurements were made twice by two investigators and were compared with the intrasurgical measurements, which served as the reference. Furcation defect volumes generated from HD-CBCT and LD-CBCT imaging were segmented by one rater. Cohen's kappa and intraclass correlation coefficient (ICC) values were calculated to determine intra- and interrater reliability. The level of significance was set at α = 0.05. In total, 59 furcation defects of nine upper and 16 lower human molars were assessed. Comparing CBCT furcation defect measurements with surgical measurements revealed a Cohen's kappa of 0.5975 (HD-and LD-CBCT), indicating moderate agreement. All furcation defects identified by HD-CBCT were also detected by LD-CBCT by both raters, resulting in a Cohen's kappa of 1. For interrater agreement, linear furcation defect measurements showed an ICC of 0.992 for HD-CBCT and 0.987 for LD-CBCT. The intrarater agreement was 0.994(r1)/0.992(r2) for HD-CBCT and 0.987(r1)/0.991(r2) for LD-CBCT. The intermodality agreement was 0.988(r1)/0.991(r2). Paired t-test showed no significant differences between HD-CBCT and LD-CBCT measurements. LD-CBCT is a precise and reliable method for detecting and measuring furcation defects in mandibular and maxillary molars in this experimental setting. It has the potential to improve treatment planning and treatment monitoring with a far lower radiation dose than conventional HD-CBCT.
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