CT dose management of adult patients with unknown body weight using an effective diameter

[Accuracy of Effective Diameter and Water Equivalent Diameter Using Phantoms in Various CT Systems]

PURPOSE

The effects of scanning parameters such as CT system performance, CT bed geometry, and upper limb position on effective diameter (ED) and water equivalent diameter (WED) have not been assessed. The purpose of this study was to compare both ED and WED obtained with various CT systems with theoretical values and to assess their accuracy.

METHODS

Jaszczak cylindrical phantom (Data Spectrum, Durham, NC, USA), NEMA IEC body phantom (AcroBio, Tokyo), and thoracic bone phantom were used in this study with and without upper limb phantom. The ED, WED, and size-specific dose estimate (SSDE) obtained using 8 types of CT systems were computed using radiation dose control software.

RESULTS

The EDs had <5% error for all systems, but the error increased as the aspect ratio of the phantom increased. The accuracy of WED varied depending on the CT systems, with a maximum difference of 3.57 cm between systems. The influence of the upper limb depended on the shape of the bed of the CT systems, which affected the correlation between ED as well as WED and SSDE.

CONCLUSION

Although the ED did not show any dependence on the CT system, the accuracy of WED for fusion CT was low. We found that there are issues in the management of scanning data, including the upper limb.

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

PURPOSE

Currently, 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.

METHODS

A 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.

RESULTS

Our 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.

CONCLUSIONS

The 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.

Estimating Body Weight From Measurements From Different Single-Slice Computed Tomography Levels: An Evaluation of Total Cross-Sectional Body Area Measurements and Deep Learning

OBJECTIVE

This study aimed to evaluate the correlation between the estimated body weight obtained from 2 easy-to-perform methods and the actual body weight at different computed tomography (CT) levels and determine the best reference site for estimating body weight.

METHODS

A total of 862 patients from a public database of whole-body positron emission tomography/CT studies were retrospectively analyzed. Two methods for estimating body weight at 10 single-slice CT levels were evaluated: a linear regression model using total cross-sectional body area and a deep learning-based model. The accuracy of body weight estimation was evaluated using the mean absolute error (MAE), root mean square error (RMSE), and Spearman rank correlation coefficient ( ρ ).

RESULTS

In the linear regression models, the estimated body weight at the T5 level correlated best with the actual body weight (MAE, 5.39 kg; RMSE, 7.01 kg; ρ = 0.912). The deep learning-based models showed the best accuracy at the L5 level (MAE, 6.72 kg; RMSE, 8.82 kg; ρ = 0.865).

CONCLUSIONS

Although both methods were feasible for estimating body weight at different single-slice CT levels, the linear regression model using total cross-sectional body area at the T5 level as an input variable was the most favorable method for single-slice CT analysis for estimating body weight.

Radiation dose management system in computed tomography procedures: a systematic review

A systematic literature review was carried out to explore articles that reported the use of radiation dose management systems (RDMSs) in computed tomography (CT). The preferred reporting items for systematic review and meta-analysis flow chart were used to screen articles in PubMed, EBSCOhost, Web of Science, SCOPUS and Cochrane Library. A total of 1041 articles were retrieved and screened. After evaluation against criteria, 38 articles were selected and synthesised narratively. The results revealed that several RDMSs have been used in CT. The review also indicated that the use of RDMSs has promoted the implementation of diagnostic reference levels for dose optimisation. A RDMS, such as DoseWatch, is associated with compatibility challenges and failure in data transmission, while manual RDMSs are cumbersome and prone to data entry errors. Thus, a robust automated RDMS that is compatible with the different CT systems would provide efficient CT dose management.

Prediction of body weight from chest radiographs using deep learning with a convolutional neural network

Accurate body weights are not necessarily available in routine clinical practice. This study aimed to investigate whether body weight can be predicted from chest radiographs using deep learning. Deep-learning models with a convolutional neural network (CNN) were trained and tested using chest radiographs from 85,849 patients. The CNN models were evaluated by calculating the mean absolute error (MAE) and Spearman's rank correlation coefficient (ρ). The MAEs of the CNN models were 2.63 kg and 3.35 kg for female and male patients, respectively. The predicted body weight was significantly correlated with the actual body weight (ρ = 0.917, p < 0.001 for females; ρ = 0.915, p < 0.001 for males). The body weight was predicted using chest radiographs by applying deep learning. Our method is potentially useful for radiation dose management, determination of the contrast medium dose, and estimation of the specific absorption rate in patients with unknown body weights.

[Organ and Effective Doses Using Automation Organ Dose Estimation Software for Lung Cancer Screening Using Low-dose Computed Tomography]

PURPOSE

The purpose of this study was to evaluate the differences in the organ doses and the effective doses using three types of automated organ dose estimation software for low-dose computed tomography (CT) screening for lung cancer and to evaluate the correlations between each dose and size-specific dose estimates (SSDEs).

METHODS

Seventy-two adults who underwent low-dose CT screening for lung cancer were included, and the organ doses and the effective doses were calculated using each of automated organ dose estimation software. We evaluated differences between software for the organ doses and the effective doses and the correlations between each dose and SSDEs.

RESULTS

Differences in organ doses and effective doses were observed among the software. The organ doses showed a strong correlation (r=0.833-0.995) with SSDEs for organs within the scan range. The effective doses showed a strong correlation (r=0.830-0.970) with SSDEs, although there were significant differences among the software.

CONCLUSION

Although the organ doses and the effective doses differed between software, it may be possible to estimate them from SSDEs by using linear regression equations.

A deep-learning method using computed tomography scout images for estimating patient body weight

Body weight is an indispensable parameter for determination of contrast medium dose, appropriate drug dosing, or management of radiation dose. However, we cannot always determine the accurate patient body weight at the time of computed tomography (CT) scanning, especially in emergency care. Time-efficient methods to estimate body weight with high accuracy before diagnostic CT scans currently do not exist. In this study, on the basis of 1831 chest and 519 abdominal CT scout images with the corresponding body weights, we developed and evaluated deep-learning models capable of automatically predicting body weight from CT scout images. In the model performance assessment, there were strong correlations between the actual and predicted body weights in both chest (ρ = 0.947, p < 0.001) and abdominal datasets (ρ = 0.869, p < 0.001). The mean absolute errors were 2.75 kg and 4.77 kg for the chest and abdominal datasets, respectively. Our proposed method with deep learning is useful for estimating body weights from CT scout images with clinically acceptable accuracy and potentially could be useful for determining the contrast medium dose and CT dose management in adult patients with unknown body weight.