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Structural Similarity Index 인자를 이용한 방사선 분할 조사간 환자 체위 변화의 자동화 검출능 평가: 초기 보고

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

  • 윤한빈 (양산부산대학교병원 방사선종양학과) ;
  • 전호상 (양산부산대학교병원 방사선종양학과) ;
  • 이자영 (양산부산대학교병원 방사선종양학과) ;
  • 이주혜 (양산부산대학교병원 방사선종양학과) ;
  • 남지호 (양산부산대학교병원 방사선종양학과) ;
  • 박달 (부산대학교병원 방사선종양학과) ;
  • 김원택 (부산대학교 의학전문대학원 방사선종양학교실) ;
  • 기용간 (부산대학교 의학전문대학원 방사선종양학교실) ;
  • 김동현 (부산대학교병원 방사선종양학과)
  • Youn, Hanbean (Department of Radiation Oncology, Pusan National University Yangsan Hospital) ;
  • Jeon, Hosang (Department of Radiation Oncology, Pusan National University Yangsan Hospital) ;
  • Lee, Jayeong (Department of Radiation Oncology, Pusan National University Yangsan Hospital) ;
  • Lee, Juhye (Department of Radiation Oncology, Pusan National University Yangsan Hospital) ;
  • Nam, Jiho (Department of Radiation Oncology, Pusan National University Yangsan Hospital) ;
  • Park, Dahl (Department of Radiation Oncology, Pusan National University Hospital) ;
  • Kim, Wontaek (Department of Radiation Oncology, Pusan National University School of Medicine) ;
  • Ki, Yongkan (Department of Radiation Oncology, Pusan National University School of Medicine) ;
  • Kim, Donghyun (Department of Radiation Oncology, Pusan National University Hospital)
  • 투고 : 2015.12.09
  • 심사 : 2015.12.22
  • 발행 : 2015.12.31

초록

현대 방사선치료는 고선명 X선 투사영상을 이용하여 환자 및 종양의 위치를 확인하는 기술이 요구되지만, 3차원 영상 촬영을 위한 피폭량 및 영상정보의 급격한 증가는 환자에게 추가적인 부담이 될 수 있다. 본 연구에서는 영상의 구조 정보를 효과적으로 추출할 수 있는 Structural similarity (SSIM) 인자를 도입하여 매일 촬영하는 환자의 2차원 X선 영상간 차이를 자동 분석하여 환자의 위치 정확성의 검증 가능성을 제시하였다. 먼저 종양을 모사하기 위하여 구형 전산 팬텀의 크기와 위치를 변화시키면서 각각의 투사 영상을 시뮬레이션하고, SSIM 인자를 통해 영상 간 차이를 검출하여 분석하였다. 또한 12일간 매일 촬영한 방사선 치료 환자의 2차원 X선 영상들 간 차이를 동일한 방법으로 검출하였다. 그 결과 산출된 팬텀 변화에 따른 SSIM 값은 0.85~1 범위로, 관심영역(ROI)을 영상 전체가 아닌 팬텀으로 한정하였을 때는 0.006~1 범위로 나타나서 ROI 적용 시 민감도가 크게 상승하는 것을 확인하였다. 또한 임상 영상의 SSIM은 0.799~0.853 범위의 값을 나타냈으며 영상 간 차이가 SSIM 분포 상에 검출되는 것을 확인하였다. 본 연구결과는 소요 시간 및 피폭 등의 우려로 매일 사용하기 어려운 3차원 영상기법 대신 간단한 2차원 영상만을 이용하여 객관적이고 정량적인 환자 위치 정확성의 자동 평가 기법을 제공할 수 있을 것으로 기대된다.

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.

키워드

참고문헌

  1. Paquin D, Levy D, Xing D: Multiscale registration of planning CT and daily cone beam CT images for adaptive radiation therapy. Med phys 36:4-11 (2009) https://doi.org/10.1118/1.3026602
  2. Yeung AR, Li JG, Shi W, Newlin HE, et al: Tumor localization using cone-beam CT reduces setup margins in conventionally fractionated radiotherapy for lung tumors. Int J Radiat Oncol Biol Phys 74:1100-1107 (2009) https://doi.org/10.1016/j.ijrobp.2008.09.048
  3. Elstrom UV, Wysocka BA, Muren LP, et al: Daily kV cone-beam CT and deformable image registration as a method for studying dosimetric consequences of anatomic changes in adaptive IMRT of head and neck cancer. Acta oncologica 49: 1101-1108 (2010) https://doi.org/10.3109/0284186X.2010.500304
  4. Ost P, De Meerleer G, De Gersem W, et al: Analysis of prostate bed motion using daily cone-beam computed tomography during postprostatectomy radiotherapy. Int J Radiat Oncol Biol Phys 79:188-194 (2011) https://doi.org/10.1016/j.ijrobp.2009.10.029
  5. Veiga C, McClelland J, Moinuddin S, et al: Toward adaptive radiotherapy for head and neck patients: Feasibility study on using CT-to-CBCT deformable registration for "dose of the day" calculations. Med Phys 41:031703 (2014) https://doi.org/10.1118/1.4864240
  6. Wang Z, Bovik AC, Sheikh HR, et al: Image quality assessment: from error visibility to structural similarity. IEEE Trans Imag Proc 13:600-612 (2004) https://doi.org/10.1109/TIP.2003.819861
  7. Brunet D, Vrscay ER, Wang Z: On the mathematical properties of the structural similarity index. IEEE Trans Imag Proc 21:1488-1499 (2012) https://doi.org/10.1109/TIP.2011.2173206
  8. Chai L, Sheng Y: Optimal design of multichannel equalizers for the structural similarity index. IEEE Trans Imag Proc 23: 5626-5637 (2014) https://doi.org/10.1109/TIP.2014.2367320
  9. Channappayya SS, Bovik AC, Caramanis C et al.: Design of linear equalizers optimized for the structural similarity index. IEEE Trans Imag Proc 17:857-872 (2008) https://doi.org/10.1109/TIP.2008.921328
  10. Channappayya SS, Bovik AC, Heath, RW Jr.: Rate bounds on SSIM index of quantized images. IEEE Trans Imag Proc 17:1624-1639 (2008) https://doi.org/10.1109/TIP.2008.2001400
  11. Charrier C, Knoblauch K, Maloney LT: Optimizing multiscale SSIM for compression via MLDS. IEEE Trans Imag Proc 21:4682-4694 (2012) https://doi.org/10.1109/TIP.2012.2210723
  12. Kolaman A, Yadid-Pecht O: "Quaternion structural similarity: a new quality index for color images. IEEE Trans Imag Proc 21:1526-1536 (2012) https://doi.org/10.1109/TIP.2011.2181522
  13. Rehman A, Zhou R: Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans Imag Proc 21:3378-3389 (2012) https://doi.org/10.1109/TIP.2012.2197011
  14. Sampat MP, Wang Z, Gupta S: Complex wavelet structural similarity: a new image similarity index. IEEE Trans Imag Proc 18:2385-2401 (2009) https://doi.org/10.1109/TIP.2009.2025923
  15. Wang S, Rehman A, Wang Z: Perceptual video coding based on SSIM-inspired divisive normalization. IEEE Trans Imag Proc 22:1418-1429 (2013) https://doi.org/10.1109/TIP.2012.2231090
  16. Zujovic J, Pappas TN, Neuhoff DL: Structural texture similarity metrics for image analysis and retrieval. IEEE Trans Imag Proc 22:2545-2558 (2013) https://doi.org/10.1109/TIP.2013.2251645
  17. Siddon RL: Fast calculation of the exact radiological path for a three-dimensional CT array. Med Phys 12:252-255 (1985) https://doi.org/10.1118/1.595715
  18. De Man B, Basu S: Distance-driven projection and backprojection in three dimensions. Physics in medicine and biology 49:2463-2475 (2004) https://doi.org/10.1088/0031-9155/49/11/024