Effectiveness of body size stratification for patient exposure optimization in Computed Tomography. Issue 163 (June 2023) - De Monte, Francesca; Sapignoli, Sonia; Laura Cortinovis, Anna; Di Maggio, Antonio; Nardin, Margherita; Pizzirani, Enrico; Scagliori, Elena; Volpe, Alessandro; Paiusco, Marta; Roggio, Antonella
Abstract: Purpose: To establish size-dependent DRL and to estimate the effectiveness of the size-dependent DRLs over size-independent DRLs for a CT exposure optimization process. Methods: The study included 16, 933 adult CT body examinations of the most common CT protocols. Acquisitions were included following an image quality assessment. Patients were grouped into five different classes by means of the water equivalent diameter (Dw ): 21 ≤ Dw < 25, 25 ≤ Dw < 29, 29 ≤ Dw < 33, 33 ≤ Dw < 37 (in cm). CTDIvol, DLP, DLPtot. and SSDE median values were provided both for the sample as a whole ( size-independent approach) and for each Dw class ( size-dependent approach). The performance of the two approaches in classifying sub-optimal examinations was evaluated through the confusion matrix and Matthews Correlation Coefficient (MCC) metric. The 75th percentile of the CTDIvol distribution was arbitrarily chosen as a threshold level above which the acquisitions are considered sub-optimal. Results: CTDIvol, DLP, DLPtot and SSDE typical values (median values) are statistically different across Dw groups. The confusion matrix analysis suggests that size-independent DRL could not mark potential suboptimal protocols for small and large patients. The agreement between the size-dependent and size-independent methods is strong only for the most populous classes (MCC > 0.7). For small and large patients size-independent approach fails to identify as sub-optimal around 20 % of the acquisition (MCC≪0.2). Conclusions: It was proven by means of the confusion matrix and MCC metric that stratifying DRLs by patient size, size-dependent DRL can be a powerful strategy in order to improve the dose optimization process shown that a size-independent DRL fails to identify sub-optimal examinations for small and large patients.