Method of determining geometric patient size surrogates using localizer images in CT

Abstract

Purpose Size‐specific dose estimates (SSDE) requires accurate estimates of patient size surrogates. AAPM Report 204 shows that the SSDE is the product of CTDIvol and a scaling factor, the normalized dose coefficient (NDC) which depends on patient size surrogates for CT axial images. However, SSDE can be determined from CT localizer prior to CT scanning. AAPM Report 220 charges that a magnification correction is needed for geometric patient size‐surrogates. In this study, we demonstrate a novel “model‐based” magnification correction on patient data. Methods 573 patient scans obtained from a clinical CT system including 229 adult abdomen, 284 adult chest, 48 pediatric abdomen, and 12 pediatric chest exams. LAT and AP dimensions were extracted from CT localizers using a threshold extraction method (the ACR DIR). The model‐based magnification correction was applied to the AP and LAT dimensions extracted using the ACR DIR. NDC was calculated using the effective diameter for the ACR DIR only, the model‐based localizer‐based and axial‐based approaches. The LAT and AP dimensions were extracted from the “gold” standard CT axial scans. Outliers are defined as points outside the 95% confidence intervals and were analyzed. Results NDC estimates for the localizer‐based model‐based approach had an excellent correlation (R2 = 0.92) with the gold standard approach. The effective diameter for ACR DIR and model‐based approaches are 8.0% and 1.0% greater than the gold standard respectively. Outliers were determined to be primarily patient truncation, with arms down or with devices. ACR DIR size extraction method fails for bariatric patients where the threshold is too high and some of their anatomy was included in the CT couch, and small patients due to the CT couch being included in the size measurement. Conclusion The model‐based magnification method gives an accurate estimate of patient size surrogates extracted from CT localizers that are needed for calculating NDC to achieve accurate SSDE.

Deep Learning-based calculation of patient size and attenuation surrogates from localizer Image: Toward personalized chest CT protocol optimization

“…Anam et al reported 5.4 ± 4.2 % and 2.3 ± 3.2 % errors in calculating T W in an anthropomorphic phantom study and 30 clinical cases, respectively [10]. Our results showed similar R 2 achieved by the proposed metric compared to Burton et al [24], wherein they used both AP and lateral images simultaneously. We only used a single AP localizer commonly acquired in clinical protocols.…”

“…They validated their model on body CTDI phantom, an anthropomorphic phantom, and 30 clinical studies, reporting an average error in DW calculation of 5.4 ± 4.2 % compared with axial images serving as ground truth (GT). Burton et al [24] reported a good agreement between size metrics estimated from two AP and lateral images and the metrics measured from axial CT images on 284 chest CT images with R 2 = 0.92. Terashima et al [20] evaluated the effect of TH on the accuracy of DW estimates from the AP localizer and found a suitable CF based on TH with an error equal to 2.4 % in an anthropomorphic phantom study.…”

Estimating size specific dose estimate from computed tomography radiograph localizer with radiation risk assessment

“…According to the AAPM TG Report 220, using the water-equivalent diameter (WED) is the "preferred" method in determining the SSDE because it factors in patient attenuation properties. Burton and Szczykutowicz 9 demonstrated that calculating patient size surrogates such as WED from patient data was similar to the results of the AAPM TG Reports 204 and 220, 10 thus providing the medical imaging community with a gold standard for calculating WED from patient data. Zhang et al determined a calibration method that may be used to determine the WED from CT localizers using phantoms, 11 however it has not been extended to patient scans.…”

“…Estimating WED from CT localizer radiographs would allow for WED to be included into data-driven clinical workflows such as size adaptive protocol selection using diagnostic reference ranges which provide a minimum estimated patient dose. 17,18 The additional benefits of using CT localizers include reduced data overhead if CT axial images are not stored and possible limitations such as couch removal 10,19 related to CT axial calculation.…”

Estimating patient water equivalent diameter from CT localizer images – A longitudinal and multi‐institutional study of the stability of calibration parameters

“…Previous studies reported that the localizer-based patient size estimation, especially with localizer in the AP or PA directions, is subject to errors resulted from magnification or minification, and proposed various correction schemes to address these errors. 2,18,[27][28][29] In our study, we intentionally only tested lateral localizers and ensured that the phantoms were imaged at the isocenter of the scanners. With these setups, the impact of magnification or minification was minimized.…”