A Segmentation-based Automated Calculation of Patient Size and Size-specific Dose Estimates in Pediatric Computed Tomography Scans

Autor: Muhammad Kabir Abdulkadir, Noor Diyana Osman, Anusha Achuthan, Radin A. Nasirudin, Muhammad Zabidi Ahmad, Noor Hasyima Mat Zain, Ibrahim Lutfi Shuaib
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Medical Physics, Vol 49, Iss 3, Pp 456-463 (2024)
Druh dokumentu: article
ISSN: 0971-6203
1998-3913
DOI: 10.4103/jmp.jmp_26_24
Popis: Background and Purpose: Size-specific dose estimates (SSDE) have been introduced into computed tomography (CT) dosimetry to tailor patients’ unique sizes to facilitate accurate CT radiation dose quantification and optimization. The purpose of this study was to develop and validate an automated algorithm for the determination of patient size (effective diameter) and SSDE. Materials and Methods: A MATLAB platform was used to develop software of algorithms based on image segmentation techniques to automate the calculation of patient size and SSDE. The algorithm was used to automatically estimate the individual size and SSDE of four CT dose index phantoms and 80 CT images of pediatric patients comprising head, thorax, and abdomen scans. For validation, the American Association of Physicists in Medicine (AAPM) manual methods were used to determine the patient’s size and SSDE for the same subjects. The accuracy of the proposed algorithm in size and SSDE calculation was evaluated for agreement with the AAPM’s estimations (manual) using Bland–Altman’s agreement and Pearson’s correlation coefficient. The normalized error, system bias, and limits of agreement (LOA) between methods were derived. Results: The results demonstrated good agreement and accuracy between the automated and AAPM’s patient size estimations with an error rate of 1.9% and 0.27% on the patient and phantoms study, respectively. A 1% percentage difference was found between the automated and manual (AAPM) SSDE estimates. A strong degree of correlation was seen with a narrow LOA between methods for clinical study (r > 0.9771) and phantom study (r > 0.9999). Conclusion: The proposed automated algorithm provides an accurate estimation of patient size and SSDE with negligible error after validation.
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