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An evaluation of organ dose modulation on a GE optima CT660-computed tomography scanner.

ABSTRACT: Organ Dose Modulation or ODM (GE Healthcare, Milwaukee, WI) was evaluated to characterize changes in CTDIvol, image noise, effective dose, and organ dose saving to patients. Three separate investigations were completed: a tube current modulation phantom was scanned with and without ODM, a CTDIvol phantom was scanned with ODM, and Monte Carlo simulations were performed. ODM was found to reduce the CTDIvol by approximately 20% whilst increasing the noise by approximately 14%. This was reflected in the dose distribution, where the anterior peripheral dose was reduced by approximately 40% whilst the identical poste-rior dose remained largely unaffected. Enabling ODM for the entire scan would reduce the effective dose by approximately 24%; however, this saving reduces to 5% if the images are matched for CTDIvol. These savings mostly originated from reductions in dose to the stomach, breasts, colon, bladder, and liver. ODM has the effect of a global reduction in CTDIvol with an associated increase in image noise. The benefit of ODM was found to be reduced when the dose-saving contribution from the reduced CTDIvol was removed. Given that there is a higher contribution to effective dose throughout the body from the anterior projections, consideration should be given to applying ODM throughout.

SUBMITTER: Dixon MT

PROVIDER: S-EPMC5690941 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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An evaluation of organ dose modulation on a GE optima CT660-computed tomography scanner.

Dixon Matthew T MT Loader Robert J RJ Stevens Gregory C GC Rowles Nick P NP

Journal of applied clinical medical physics 20160508 3

Organ Dose Modulation or ODM (GE Healthcare, Milwaukee, WI) was evaluated to characterize changes in CTDIvol, image noise, effective dose, and organ dose saving to patients. Three separate investigations were completed: a tube current modulation phantom was scanned with and without ODM, a CTDIvol phantom was scanned with ODM, and Monte Carlo simulations were performed. ODM was found to reduce the CTDIvol by approximately 20% whilst increasing the noise by approximately 14%. This was reflected in ...[more]

PMID: 27167255

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Project description:BackgroundLow-dose computed tomography (LDCT) reduces radiation exposure, but the introduced noise and artifacts impair its diagnostic accuracy. Convolutional neural networks (CNNs) are widely used for LDCT denoising, but they suffer from a limited receptive field. The use of a larger kernel size can enlarge the receptive field and boost model performance; however, the computational cost of the model greatly increases. We aimed to develop a LDCT denoising CNN with a large receptive field and lower computational complexity.MethodsWe developed a multi-scale perceptual modulation network (MSPMnet) incorporating a powerful multi-head decomposable convolution (MHDC). To address the high computational complexity of large kernel convolutions, we developed a novel MHDC module that can capture multi-scale features and efficiently expand the receptive field. The MHDC module couples maximum-pooling with three depth-wise convolutions of varying kernel sizes via a channel splitting mechanism, where, unlike conventional CNNs, the two large two-dimensional kernels are each decomposed into a set of cascaded orthogonal one-dimensional kernels to remain lightweight. Further, departing from prior methodologies that apply a uniform kernel size throughout the network, we introduced a receptive field-ramp mechanism that adeptly transitions from local to relatively long-range dependency modeling as the network depth increases, thereby achieving superior performance.ResultsThe proposed MSPMnet was evaluated on a Mayo Clinic data set with a conventional iterative algorithm, two CNN models, and two Transformer models used for comparison. Compared to the competing baseline methods, the MSPMnet exhibited better performance in both the visual and quantitative assessments. Visually, the MSPMnet preserved the structure, edges, and textures with excellent noise and artifact reduction, generating the denoised images closest to normal-dose computed tomography images. Quantitatively, the MSPMnet had the lowest root mean-square error (RMSE) (8.3094±1.9325) and the highest peak signal-to-noise ratio (PSNR) (33.8525±1.8213 dB), structural similarity index (SSIM) (0.9309±0.0272), and feature similarity index (FSIM) (0.9699±0.0113), demonstrating superior denoising performance.ConclusionsThe proposed MSPMnet excelled at LDCT denoising, effectively removing noise and artifacts while preserving edges. Compared to the state-of-the-art CNNs and Transformers, the proposed MSPMnet exhibited superior denoising performance both quantitatively and qualitatively.

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.

Dataset Information

An evaluation of organ dose modulation on a GE optima CT660-computed tomography scanner.

Publications

An evaluation of organ dose modulation on a GE optima CT660-computed tomography scanner.

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