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DOI 10.6009/jjrt.2024-1500

Медицинская библиотека · Nihon Hoshasen Gijutsu Gakkai zasshi · 2024

[A Comparison of Tumor Respiratory Motion Evaluation Methods Using Dynamic Thorax Motion Phantom].

Kamima Tatsuya, Akashi Kana, Watanabe Shiori, Matsubayashi Fumiyasu, Tachibana Rie, Ito Yasushi

СтатьяСравнительное исследованиеАннотация на английскомПеревод пока не подготовлен

Аннотация

PURPOSE: We evaluated the measurement accuracy and time efficiency of the tumor respiratory motion evaluation methods using a dynamic thorax motion phantom.

METHODS

A total of 12 patterns of 4DCT images with different tumor displacements and artifacts were used for the measurement. Three methods were employed to measure tumor motion. The first method was the manual delineation of the tumor on each phase CT image with a treatment planning system (RTPS [Manual]). The second method was the automatic delineation of the tumor structure by deformation and copying (RTPS [Auto]). The third method was tumor motion analysis software (Simple 4D Analysis Ver.1.3.1 [Simple 4D]; Triangle Products, Chiba, Japan). For each method, the difference between the phantom motion and the measured value was determined.

RESULTS

The differences (mean±standard deviation: SD) in the superior-inferior direction for RTPS (Manual), RTPS (Auto), and Simple 4D in the without-artifact images were -0.6 mm±0.6 mm, -5.0 mm±2.2 mm, and -1.0 mm±0.0 mm, respectively. The difference in the left-right and anterior-posterior directions was within 1 mm for all methods. Furthermore, the time required for Simple 4D was shorter than for the other methods.

CONCLUSION

Simple 4D showed the comparable measurement accuracy and improvement time efficiency to RTPS (Manual) and RTPS (Auto), and was useful for tumor respiratory motion analysis.

Для цитирования: Kamima Tatsuya, Akashi Kana, Watanabe Shiori, Matsubayashi Fumiyasu, Tachibana Rie, Ito Yasushi. [A Comparison of Tumor Respiratory Motion Evaluation Methods Using Dynamic Thorax Motion Phantom].. Nihon Hoshasen Gijutsu Gakkai zasshi. 2024. DOI: 10.6009/jjrt.2024-1500.

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