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Respiratory Motion Management On Patient Body Surface Using 3D Scanning Technology

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C Nakagami

C Nakagami1*, N Hayashi2 , T Matsunaga3 , Y Adachi4 , M Nozue5 , H Yamamoto6 , Y Shirakami7 , (1) ,,,(2) School of Health Sciences, Fujita Health University, Toyoake, Aichi, (3) ,,,(5) Seirei Hamamatsu General Hospital, Hamamatsu, Shizuoka, (6) ,,,(7) Electronics Research & Development Corporation, Okayama, Okayama

Presentations

MO-RAM-GePD-T-5 (Monday, July 31, 2017) 9:30 AM - 10:00 AM Room: Therapy ePoster Lounge


Purpose: Recently, three-dimensional scanning devices for patient setup and/or monitoring during radiation delivery are widely used in radiotherapy. However, respiratory motion in radiotherapy is controlled at only one or two points on the central axis of the body surface generally. The aims of this research are to manage patient’s respiration by detecting several points to analyze the respiratory phase and to estimate suitable points automatically where the respiratory phase can be tracked accurately except on the central axis of the body surface.

Methods: We used VOXELAN (ERD Corporation) that can obtain 3D patient’s body surface information. In this research, anthropomorphic phantom was used for fundamental examinations. The 3D surface scanning was performed for the phantom which periodic and/or random waves were set on. The 3D data were divided into 3 × 3 cm meshed region of interest (ROI). On each ROI, the auto-correlation-function (ACF) was calculated to evaluate periodicity of respiration wave. Then the desirable ACFs on all ROIs were ranked. Next, the cross-correlation-function (CCF) were calculated between ROIs ranked 1st to 5th.

Results: Total of 5 samples was analyzed by use of the dedicated software. The values of ACF of rank 1st to 5th ROIs showed approximately 1. In periodic wave, the strong correlation was observed. The value of CCF showed from 0.9 to 1.0.In periodic wave strong correlation coefficients was observed. However, lower CCF was observed in random wave even though desirable value of ACF was observed.

Conclusion: Respiratory phase of rank 1st to 5th ROIs with both periodicity and similar respiratory phase can be tracked between different ROIs. Consequently, it is suggested that patient’s respiration can be controlled by analysis of 3D information. With combination of ACF and CCF analyses, suitable points for respiratory monitoring are estimated automatically.


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