Quality assurance in CT: implementation of the updated national diagnostic reference levels using an automated CT dose monitoring system

Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment

Purpose

This study aimed to evaluate Artificial Neural Network (ANN) modeling to estimate the significant dose length product (DLP) value during the abdominal CT examinations for quality assurance in a retrospective, cross-sectional study.

Methods

The structure of the ANN model was designed considering various input parameters, namely patient weight, patient size, body mass index, mean CTDI volume, scanning length, kVp, mAs, exposure time per rotation, and pitch factor. The aforementioned examination details of 551 abdominal CT scans were used as retrospective data. Different types of learning algorithms such as Levenberg-Marquardt, Bayesian and Scaled-Conjugate Gradient were checked in terms of the accuracy of the training data.

Results

The R-value representing the correlation coefficient for the real system and system output is given as 0.925, 0.785, and 0.854 for the Levenberg-Marquardt, Bayesian, and Scaled-Conjugate Gradient algorithms, respectively. The findings showed that the Levenberg-Marquardt algorithm comprehensively detects DLP values for abdominal CT examinations. It can be a helpful approach to simplify CT quality assurance.

Conclusion

It can be concluded that outcomes of this novel artificial intelligence method can be used for high accuracy DLP estimations before the abdominal CT examinations, where the radiation-related risk factors are high or risk evaluation of multiple CT scans is needed for patients in terms of ALARA. Likewise, it can be concluded that artificial learning methods are powerful tools and can be used for different types of radiation-related risk assessments for quality assurance in diagnostic radiology.

Simplified size adjusted dose reference levels for adult CT examinations: A regional study

PURPOSE

To investigate retrospective classification of adult patients into small, average, and large based on effective diameter (EDia) from localizer image of computed tomography (CT) scans and to develop regional diagnostic reference levels (DRLs) and achievable doses (AD).

METHOD

The patients falling within the mean ± standard deviation (SD) of EDia were classified as average; those below this range as small and above as large. The CTDI_vol,dose-length-product (DLP) and size-specific dose estimates (SSDE) of all adult patients undergoing CT examinations in 8 CT facilities for 11 months (Dec. 2019 - Oct. 2020) were evaluated. The 75th and 50th percentile values were compared with national and international values.

RESULTS

Of the total of 69,434 CT examinations, nearly 80% fell within average size. The 75th percentile values of CTDI_vol and DLP for small patients for abdomen-pelvic exams were nearly half of average sized patients. Similarly, the 75th percentile values for large patients were nearly double. Similar findings were not found for chest exams. Analysis of image quality and dose factors such as noise, mean axial length, slice thickness, mean number of sequences, use of iterative reconstruction and tube current modulation (TCM) resulted in identification of opportunities for improvement and optimization of different CT facilities.

CONCLUSIONS

DRLs for adult patients were found to vary widely with patient size and thus establishing DRLs only for standard sized patient is not adequate. Simplified and intuitive methods for size classification was shown to provide meaningful information for optimization for patients outside the standard size adult.

Minimizing Radiation Dose Outliers Through Systematic Analysis, Computed Tomography Technologist Education, and Standardized System Solutions

OBJECTIVES

The aims of the study were to systematically analyze causes for radiation dose outliers in emergency department noncontrast head computed tomographies (CTs), to develop and implement standardized system solutions, and audit program success for an extended period of time.

METHODS

This study was performed in a large, tertiary academic center between January 2015 and September 2017. Four phases of radiation dose data collection with and without prior interventions were performed. Outliers from 5 categories were evaluated for appropriateness in consensus by 2 radiologists and a senior CT technologist.

RESULTS

A total of 275 ± 15 CTs per period were included. Fifty-seven inappropriate scanning parameters were found in 24 (9%) of 254 CTs during the first analysis, 27 in 21 (7%) of 290 CTs during the second, 11 in 10 (4%) of 276 during the third assessment (P = 0.006). After a year without additional intervention, the number remained stable (14 in 11/281 CTs, 4%).

CONCLUSIONS

Combining a dose reporting system, individual case analysis, staff education, and implementation of systemic solutions lead to sustained radiation exposure improvement.

Local clinical diagnostic reference levels for chest and abdomen CT examinations in adults as a function of body mass index and clinical indication: a prospective multicenter study

OBJECTIVES

To compare institutional dose levels based on clinical indication and BMI class to anatomy-based national DRLs (NDRLs) in chest and abdomen CT examinations and to assess local clinical diagnostic reference levels (LCDRLs).

METHODS

From February 2017 to June 2018, after protocol optimization according to clinical indication and body mass index (BMI) class (< 25; ≥ 25), 5310 abdomen and 1058 chest CT series were collected from 5 CT scanners in a Swiss multicenter group. Clinical indication-based institutional dose levels were compared to the Swiss anatomy-based NDRLs. Statistical significance was assessed (p < 0.05). LCDRLs were calculated as the third quartile of the median dose values for each CT scanner.

RESULTS

For chest examinations, dose metrics based on clinical indication were always below P75 NDRL for CTDI_vol (range 3.9-6.4 vs. 7.0 mGy) and DLP (164.0-211.2 vs. 250 mGycm) in all BMI classes except for DLP in BMI ≥ 25 (248.8-255.4 vs. 250.0 mGycm). For abdomen examinations, they were significantly lower or not different than P50 NDRLs for all BMI classes (3.8-9.0 vs. 10.0 mGy and 192.9-446.8 vs. 470mGycm). The estimated LCDRLs show a drop in CTDI_vol (21% for chest and 32% for abdomen, on average) with respect to current DRLs. When considering BMI stratification, the largest LCDRL difference within the same clinical indication is for renal tumor (4.6 mGy for BMI < 25 vs. 10.0 mGy for BMI ≥ 25; - 117%).

CONCLUSION

The results suggest the necessity of estimating clinical indication-based DRLs, especially for abdomen examinations. Stratifying per BMI class allows further optimization of the CT doses.

KEY POINTS

• Our data show that clinical indication-based DRLs might be more appropriate than anatomy-based DRLs and might help in reducing large variations in dose levels for the same type of examinations. • Stratifying the data per patient-size subgroups (non-overweight, overweight) allows a better optimization of CT doses and therefore the possibility to set LCDRLs based on BMI class. • Institutions who are fostering continuous dose optimization and LDRLs should consider defining protocols based on clinical indication and BMI group, to achieve ALARA.