Bridging TG-116 and TG-232: A retrospective multi-vendor evaluation of deviation index re-centering and process control in digital radiography

Abstract

Background

The deviation index (DI) is a vendor-neutral indicator of detector exposure relative to the configured target exposure index (TEI). Because TEI is a protocol value rather than a calibrated physical quantity, poorly aligned TEI settings can create systematic DI offsets, obscure protocol problems, and prevent meaningful statistical process control.

Objective

This retrospective multi-vendor study evaluated whether routine DICOM metadata can identify biased DI distributions, separate fixed TEI-setting offsets from protocol-adherence variation, estimate candidate TEI updates, and support TG-232-aligned standard deviation-based monitoring after DI re-centering.

Methods

We retrospectively analyzed 38 916 digital radiography examinations performed from January 1, 2024 through August 27, 2024 using three vendor platforms at a tertiary hospital. DICOM metadata were benchmarked against published technical standards using a composite adherence index (CAI) for SID, kVp, mAs, grid use, and AEC use. A secondary gradient-boosting/SHAP analysis was retained as a non-causal variance discovery to rank the metadata patterns used to predict DI. Candidate TEI updates were estimated within the vendor-region-projection groups by algebraically re-centering the observed mean DI ($\overline{DI})$ to 0.0. Robustness was assessed using sensitivity analyses restricted by automatic exposure control, beam energy, and patient habitus proxy measures.

Results

The baseline $\overline{DI}$ = −3.1 (SD 3.7), and only 11.3% of examinations were within |DI| ≤ 1. Forty of 45 vendor–region–projection groups (88.9%) had an absolute group $\overline{DI}$ greater than 1.0. Separately, the mean CAI was 0.176. The secondary SHAP analysis ranked the configured TEI, manufacturer, SID, exposure, and kVp as high-contribution predictors of DI; these findings were interpreted as predictive attribution, not causal evidence. DI re-centering reduced the overall variance by 42.9% (13.5 to 7.7), increased the proportion of examinations within |DI| ≤ 1 to 34.3%, and reduced severe deviations (|DI| > 3) from 60.3% to 23.1%.

Conclusion

In this retrospective, multi-vendor study, the TEI setting review was operationalized as a structured, data-driven responsibility of a clinical site. By verifying protocol adherence, equipment performance, image quality, and dose indicators before centering the $\overline{DI}$, departments can support the conversion of the DI from a biased compliance signal into a practical process-control metric for exposure governance.