Dependence of Water-equivalent Diameter and Size-specific Dose                     Estimates on CT Tube Potential

McCollough, Cynthia H.; McCollough, Shannon; Schneider, Justine J.; Moen, Taylor R.; Weaver, Jayse; Vrieze, Thomas J.; Yu, Lifeng; Leng, Shuai

doi:10.1148/radiol.210860

Cited by 6 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The D w of the phantom plus MECT extension ring was about 30 cm. The small and medium phantom D w ’s are representative of patient D w ’s of an average pediatric (McCollough et al 2022 ) and adult (Kanal et al 2017 ) chest, respectively. The scan and reconstruction parameters outlined in tables 2 and 3 were repeated for each phantom size.…”

Section: Methodsmentioning

confidence: 99%

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Im,

Halliburton,

Mei

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

ObjectiveDeep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.MethodThe lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.ResultsDLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR. ConclusionDLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

show abstract

Section: Methodsmentioning

confidence: 99%

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Im,

Halliburton,

Mei

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

Machine learning‐based estimation of patient body weight from radiation dose metrics in computed tomography

Ichikawa,

Sawane

2024

J Applied Clin Med Phys

View full text Add to dashboard Cite

PurposeCurrently, precise patient body weight (BW) at the time of diagnostic imaging cannot always be used for radiation dose management. Various methods have been explored to address this issue, including the application of deep learning to medical imaging and BW estimation using scan parameters. This study develops and evaluates machine learning‐based BW prediction models using 11 features related to radiation dose obtained from computed tomography (CT) scans.MethodsA dataset was obtained from 3996 patients who underwent positron emission tomography CT scans, and training and test sets were established. Dose metrics and descriptive data were automatically calculated from the CT images or obtained from Digital Imaging and Communications in Medicine metadata. Seven machine‐learning models and three simple regression models were employed to predict BW using features such as effective diameter (ED), water equivalent diameter (WED), and mean milliampere‐seconds. The mean absolute error (MAE) and correlation coefficient between the estimated BW and the actual BW obtained from each BW prediction model were calculated.ResultsOur results found that the highest accuracy was obtained using a light gradient‐boosting machine model, which had an MAE of 1.99 kg and a strong positive correlation between estimated and actual BW (ρ = 0.972). The model demonstrated significant predictive power, with 73% of patients falling within a ±5% error range. WED emerged as the most relevant dose metric for BW estimation, followed by ED and sex.ConclusionsThe proposed machine‐learning approach is superior to existing methods, with high accuracy and applicability to radiation dose management. The model's reliance on universal dose metrics that are accessible through radiation dose management software enhances its practicality. In conclusion, this study presents a robust approach for BW estimation based on CT imaging that can potentially improve radiation dose management practices in clinical settings.

show abstract

Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks

et al. 2023

View full text Add to dashboard Cite

Objective We propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions. Methods The voxel-wise dose maps corresponding to each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). The dose distribution in a uniform cylinder was computed through MC calculations (SP_uniform). The density map and SP_uniform dose maps were fed into a residual deep neural network (DNN) to predict SP_MC through an image regression task. The whole-body dose maps reconstructed by the DNN and MC were compared in the 11 test cases scanned with two tube voltages through transfer learning with/without tube current modulation (TCM). The voxel-wise and organ-wise dose evaluations, such as mean error (ME, mGy), mean absolute error (MAE, mGy), relative error (RE, %), and relative absolute error (RAE, %), were performed. Results The model performance for the 120 kVp and TCM test set in terms of ME, MAE, RE, and RAE voxel-wise parameters was − 0.0302 ± 0.0244 mGy, 0.0854 ± 0.0279 mGy, − 1.13 ± 1.41%, and 7.17 ± 0.44%, respectively. The organ-wise errors for 120 kVp and TCM scenario averaged over all segmented organs in terms of ME, MAE, RE, and RAE were − 0.144 ± 0.342 mGy, and 0.23 ± 0.28 mGy, − 1.11 ± 2.90%, 2.34 ± 2.03%, respectively. Conclusion Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy suitable for organ-level absorbed dose estimation. Clinical relevance statement We proposed a novel method for voxel dose map calculation using deep neural networks. This work is clinically relevant since accurate dose calculation for patients can be carried out within acceptable computational time compared to lengthy Monte Carlo calculations. Key Points • We proposed a deep neural network approach as an alternative to Monte Carlo dose calculation. • Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy, suitable for organ-level dose estimation. • By generating a dose distribution from a single source position, our model can generate accurate and personalized dose maps for a wide range of acquisition parameters.

show abstract

Dependence of Water-equivalent Diameter and Size-specific Dose Estimates on CT Tube Potential

Cited by 6 publications

References 6 publications

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Machine learning‐based estimation of patient body weight from radiation dose metrics in computed tomography

Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks

Contact Info

Product

Resources

About