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Dosimetric assessment of a single-energy metal artifact reduction algorithm for computed tomography images in
Hiroo Murazaki1, Junichi Fukunaga2, Taka-Aki Hirose2
1Division of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1 Maidashi, Fukuoka, 812-8582, Japan. murasaki@r-tec.med.kyushu-u.ac.jp.
Abstract:
This study aimed to evaluate the performance of a single-energy metal artifact reduction (SEMAR) algorithm for radiation therapy treatment using phantom cases with metal inserts, assess improvements in computed tomography (CT) number accuracy, and investigate its effects on treatment planning dosimetry. A standard electron density phantom was scanned with and without metal inserts. The numbers of tissue-equivalent materials on both uncorrected and SEMAR-corrected CT images were compared. Treatment planning accuracy was evaluated by comparing dose distributions computed using true density images (without metal inserts), uncorrected images (with metal inserts), and SEMAR-corrected images (with metal inserts) using three-dimensional gamma analysis. The numbers of the true density and uncorrected and SEMAR-corrected CT images in a muscle plug with unilateral inserts were 25.9 HU, - 281.8 HU, and 26.1 HU, respectively. A similar tendency was obtained for other tissue-equivalent materials, and the numbers on CT images were improved with the SEMAR algorithm. In cases involving 1 portal irradiation, 10-MV X-ray, and the Acuros XB algorithm, the pass ratio between the true density and uncorrected images was 89.89%, while that between the true density and SEMAR-corrected images was 95.03%. Improvements in dose distribution were evident using the SEMAR algorithm. Similar trends were found for different irradiation methods and dose calculation algorithms. The SEMAR algorithm can significantly reduce metal artifacts on CT images used for radiation treatment planning. This aspect influenced dosimetry in the region of the artifact and dose distribution was significantly improved with use of the SEMAR-corrected images.
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