Prog Med Phys.  2015 Dec;26(4):258-266. 10.14316/pmp.2015.26.4.258.

Feasibility of Automated Detection of Inter-fractional Deviation in Patient Positioning Using Structural Similarity Index: Preliminary Results

• Youn H
• Jeon H
• Lee J
• Lee J
• Nam J
• Park D
• Kim W
• Ki Y
• Kim D

• KMID: 2151760
• DOI: http://doi.org/10.14316/pmp.2015.26.4.258

Abstract

The modern radiotherapy technique which delivers a large amount of dose to patients asks to confirm the positions of patients or tumors more accurately by using X-ray projection images of high-definition. However, a rapid increase in patient's exposure and image information for CT image acquisition may be additional burden on the patient. In this study, by introducing structural similarity (SSIM) index that can effectively extract the structural information of the image, we analyze the differences between daily acquired x-ray images of a patient to verify the accuracy of patient positioning. First, for simulating a moving target, the spherical computational phantoms changing the sizes and positions were created to acquire projected images. Differences between the images were automatically detected and analyzed by extracting their SSIM values. In addition, as a clinical test, differences between daily acquired x-ray images of a patient for 12 days were detected in the same way. As a result, we confirmed that the SSIM index was changed in the range of 0.85~1 (0.006~1 when a region of interest (ROI) was applied) as the sizes or positions of the phantom changed. The SSIM was more sensitive to the change of the phantom when the ROI was limited to the phantom itself. In the clinical test, the daily change of patient positions was 0.799~0.853 in SSIM values, those well described differences among images. Therefore, we expect that SSIM index can provide an objective and quantitative technique to verify the patient position using simple x-ray images, instead of time and cost intensive three-dimensional x-ray images.

Keyword

Structural similarity; kV image; Inter-fractional deviation; Patient positioning

MeSH Terms

Humans
Patient Positioning*
Radiotherapy

Figure

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  Fig. 1. 구형 팬텀의 반지름 및 중 심 위치 변화에 대한 모식도. 이 때 r은 반지름, Δr은 반지름 변화 량, 그리고 d는 중심 위치의 이동 거리를 의미한다.

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  Fig. 2. (a) 기준 위치의 팬텀의 가 상 투사영상, (b) 팬텀의 반지름과 중심 위치 변화가 고려된 투사 영 상, (c) a와 b 영상의 차이를 나타 내는 SSIM 분포.

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  Fig. 3. 전체 산술평균을 이용한 SSIM 계산 결과: (a) 20, 40, 60, 및 80 mm 반지름을 가지는 팬텀의 중심위치 변화에 따른 SSIM 변화의 추이와 (b) 20, 40, 및 60 mm의 반지름을 가지는 팬텀에서 반지름의 변화가 발생할 때의 SSIM 경향.

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  Fig. 4. ROI을 팬텀 내부로 가정하고 계산된 SSIM 변화 추이: (a) 20, 40, 60, 및 80 mm 반지름을 가지는 팬텀의 중심위치 변화에 따른 SSIM 변화의 추이와 (b) 20, 40, 및 60 mm의 반지름을 가지는 팬텀에서 반지름의 변화가 발생할 때의 SSIM 경향.

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  Fig. 5. Δr을 −30∼30 mm, d를 0∼50 mm까지 변화시키면서 획 득된 SSIM 계산결과(ROI내의 픽셀들에 대한 산술평균을 수행).

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  Fig. 6. OBI로 촬영된 참조 투사영 상(a)과 해당영상의 히스토그램 평 활화 결과(b): 본 연구에서는 OBI 투사영상으로부터 보다 많은 구조 정보를 추출하기 위해 전체 영상 에 대해 히스토그램 평활화 연산 후 SSIM 계산을 수행하였다.

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  Fig. 7. 환자 위치 확인을 위해 12일간 매일 촬영하여 SSIM 분석한 결과 분포. (a)는 기준 영상인 첫 번째 촬영 영상이며, (b)∼(l)은 촬영 순서대로 배열한 매일 촬영한 영상들의 SSIM 분포들임.

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  Fig. 8. 기준영상을 제외한 11장의 투사영상에 대한 SSIM 계산결과(a)와 디텍터 시스템의 픽셀 수에 따른 소요 연산시간 도시(b).

Reference

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