Size-based quality-informed framework for quantitative optimization of pediatric CT

Patient-based Performance Assessment for Pediatric Abdominal CT: An Automated Monitoring System Based on Lesion Detectability and Radiation Dose

RATIONALE AND OBJECTIVE

To deploy an automated tool for evaluating pediatric body computed tomography (CT) performance utilizing metrics of radiation dose and image quality for the task of liver lesion detection.

MATERIALS AND METHODS

This IRB approved retrospective investigation used 507 IV-contrast-enhanced abdominopelvic CT scans of pediatric patients (<18 years) between June 2014 and November 2017 acquired on three scanner models from two manufacturers. The scans were evaluated in terms of radiation metrics (CTDI_vol, DLP, and SSDE) as well as task-based performance based on the clinical task of detecting a 5 mm liver lesion with a 10 HU attenuation difference from background liver. An informatics algorithm extracted a previously-validated quantitative detectability index (d') from each case reflective of the likelihood of detecting a liver lesion. The results were analyzed in terms of the relationship between d' and radiation dose metrics.

RESULTS

There was minimal SSDE variability by age. Median SSDE at 100 kV on one scanner model was 5.2 mGy (5.0-5.4 mGy interquartile range). However, when assessing image quality by applying d', the age groups separated such that the younger patients had higher d' values than older patients. Similar trends were seen in all scanners.

CONCLUSIONS

An automated method to assess clinical image quality for pediatric CT provided a metric of image quality that varied as expected across ages (i.e., higher quality for younger patients). This tool affords the establishment of a quality reference level that, in addition to dose estimations currently available, would allow for enhanced assessment (e.g., facilitated audit) of CT imaging performance.

Detection of unwarranted CT radiation exposure from patient and imaging protocol meta-data using regularized regression

BACKGROUND

Variability in radiation exposure from CT scans can be appropriate and driven by patient features such as body habitus. Quantitative analysis may be performed to discover instances of unwarranted radiation exposure and to reduce the probability of such occurrences in future patient visits. No universal process to perform identification of outliers is widely available, and access to expertise and resources is variable.

OBJECTIVE

The goal of this study is to develop an automated outlier detection procedure to identify all scans with an unanticipated high radiation exposure, given the characteristics of the patient and the type of the exam.

MATERIALS AND METHODS

This Institutional Review Board-approved retrospective cohort study was conducted from June 30, 2012 - December 31, 2013 in a quaternary academic medical center. The de-identified dataset contained 28 fields for 189,959 CT exams. We applied the variable selection method Least Absolute Shrinkage and Selection Operator (LASSO) to select important variables for predicting CT radiation dose. We then employed a regression approach that is robust to outliers, to learn from data a predictive model of CT radiation doses given important variables identified by LASSO. Patient visits whose predicted radiation dose was statistically different from the radiation dose actually received were identified as outliers.

RESULTS

Our methodology identified 1% of CT exams as outliers. The top-5 predictors discovered by LASSO and strongly correlated with radiation dose were Tube Current, kVp, Weight, Width of collimator, and Reference milliampere-seconds. A human expert validation of the outlier detection algorithm has yielded specificity of 0.85 [95% CI 0.78-0.92] and sensitivity of 0.91 [95% CI 0.85-0.97] (PPV = 0.84, NPV = 0.92). These values substantially outperform alternative methods we tested (F1 score 0.88 for our method against 0.51 for the alternatives).

CONCLUSION

The study developed and tested a novel, automated method for processing CT scanner meta-data to identify CT exams where patients received an unwarranted amount of radiation. Radiation safety and protocol review committees may use this technique to uncover systemic issues and reduce future incidents.