정보과학회 논문지 (Journal of KIISE)

  • 제43권3호
  • /
  • Pages.353-361
  • /
  • 2016
  • /
  • 2383-630X(pISSN)
  • /
  • 2383-6296(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지
DOI QR코드

DOI QR Code

치료계획용 4D MDCT와 치료 시 획득한 4D CBCT간 영상정합 및 종양 매칭을 이용한 방사선 치료 시 종양 움직임 추적

Tumor Motion Tracking during Radiation Treatment using Image Registration and Tumor Matching between Planning 4D MDCT and Treatment 4D CBCT

  • 정주립 (서울여자대학교 소프트웨어융합학과) ;
  • 홍헬렌 (서울여자대학교 소프트웨어융합학과)
  • 투고 : 2015.10.06
  • 심사 : 2016.01.15
  • 발행 : 2016.03.15
⟨ 이전 논문 다음 논문 ⟩

초록

폐암 환자의 영상유도 방사선 치료의 경우 환자의 호흡 및 심장박동에 따라 종양의 움직임이 변화할 수 있으므로 치료 시 종양의 움직임을 추적하는 것이 필요하다. 본 논문에서는 치료계획용 4D MDCT 영상과 치료 시 획득한 4D CBCT 영상의 3차원 영상 정보를 기반으로 종양 움직임을 추적하는 방법을 제안한다. 첫째, 효율적으로 치료 시 종양의 움직임을 추적하기 위해 치료계획용 4D MDCT 영상에서 획득한 종양 움직임 모델을 통해 종양의 전역적 움직임을 예측한다. 둘째, 종양 움직임 추적의 정확성을 높이기 위해 4D CBCT 영상에서 종양 주변의 구조적 정보를 이용해 세부적 움직임을 보정하여 종양의 지역적 움직임을 예측한다. 제안방법의 성능 평가를 위해 디지털 팬텀을 이용해 실험한 결과, 지역적 움직임을 고려했을 때 전역적 움직임만 보정한 경우보다 종양 위치화 오류가 45% 감소하였다.

During image-guided radiation treatment of lung cancer patients, it is necessary to track the tumor motion because it can change during treatment as a consequence of respiratory motion and cardiac motion. In this paper, we propose a method for tracking the motion of the lung tumors based on the three-dimensional image information from planning 4D MDCT and treatment 4D CBCT images. First, to effectively track the tumor motion during treatment, the global motion of the tumor is estimated based on a tumor-specific motion model obtained from planning 4D MDCT images. Second, to increase the accuracy of the tumor motion tracking, the local motion of the tumor is estimated based on the structural information of the tumor from 4D CBCT images. To evaluate the performance of the proposed method, we estimated the tracking results of proposed method using digital phantom. The results show that the tumor localization error of local motion estimation is reduced by 45% as compared with that of global motion estimation.

키워드

과제정보

연구 과제번호 : O-arm CT 융합 방사선치료기개발

연구 과제 주관 기관 : 국가과학기술연구회

참고문헌

  1. M. Mehta, R. Scrimger, R. Mackie, B. Paliwal, R. Chappell, J. Flowler, "A New Approach to Dose Escalation in Non-small-cell Lung Cancer," International Journal of Radiation Oncology, Biology, and Physics, Vol. 49, No. 1, pp. 23-33, Jan. 2001. https://doi.org/10.1016/S0360-3016(00)01374-2
  2. D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, A. A. Martinez, "Flat-panel Cone-beam Computed Tomography for Image-guided Radiation Therapy," International Journal of Radiation Oncology, Biology, and Physics, Vol. 53, No. 5, pp. 1337-1349, Aug. 2002. https://doi.org/10.1016/S0360-3016(02)02884-5
  3. P. R. Poulsen, B. Cho, P. J. Keall, "A Method to Estimate Mean Position, Motion Magnitude, Motion Correlation, and Trajectory of a Tumor from Conebeam CT Projections for Image-guided Radiotherapy," International Journal of Radiation Oncology, Biology, and Physics, Vol. 72, No. 5, pp. 1587-1596, Dec. 2008. https://doi.org/10.1016/j.ijrobp.2008.07.037
  4. J. H. Lewis, R. Li, W. T. Watkins, J. D. Lawson, W. P. Segars, L. I. Cervino, W. Y. Song, S. B. Jiang, "Markerless Lung Tumor Tracking and Trajectory Reconstruction using Rotational Conebeam Projections: a Feasibility Study," Physics in Medicine and Biology, Vol. 55, No. 9, pp. 2505-2522, May. 2010. https://doi.org/10.1088/0031-9155/55/9/006
  5. M. Chen, J. Bai, Y. Zheng, R. A. C. Siochi, "3D Lung Tumor Motion Model Extraction from 2D Projection Images of Mega-voltage Cone Beam CT via Optimal Graph Search," Proc. of Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2012, Vol. 15, Part I, LNCS 7510, pp. 239-246, 2012.
  6. Y. Yang, Z. Zhong, X. Guo, J. Wang, J. Anderson, T. Solberg, W. Mao, "A Novel Markerless Technique to Evaluate Daily Lung Tumor Motion Based on Conventional Cone-Beam CT Projection Data," International Journal of Radiation Oncology, Biology, and Physics, Vol. 82, No. 5, pp. e749-e756, Apr. 2012. https://doi.org/10.1016/j.ijrobp.2011.11.035
  7. L. Zhuang, J. Liang, D. Yan, T. Zhang, O. Marina, D. Ionascu, "An Optimization Algorithm for 3D Real-time Lung Tumor Tracking during Arc Therapy using kV Projection Images," Medical Physics, Vol. 40, No. 10, Oct. 2013.
  8. Y. Yim, H. Hong, "Correction of Segmented Lung Boundary for Inclusion of Pleural Nodules and Pulmonary Vessels in Chest CT Images," Computers in Biology and Medicine, Vol. 38, No. 3, pp. 845-857, Mar. 2008. https://doi.org/10.1016/j.compbiomed.2008.04.012
  9. H. Hong, J. Lee, Y. Yim, "Automatic Lung Nodule Matching on Sequential CT Images," Computers in Biology and Medicine, Vol. 38, No. 5, pp. 623-634, Mar. 2009. https://doi.org/10.1016/j.compbiomed.2008.02.010
  10. H. H. Jo, H. Hong, "Automatic Lung Tumor Tracking using Sequential Inter-phase Deformable Registration in 4D CT Images for Radiation Treatment Planning," Journal of KIISE: Software and Applications, Vol. 40, No. 11, pp. 684-696, Nov. 2013. (in Korean)
  11. G. Wolberg, S Zokai, "Robust Image Registration using Log-polar Transform," Proc. of International Conference on Image Processing, Vol. 1, pp. 493-496, Sep. 2000.
  12. W. P. Segars, M. Mahesh, T. J. Beck, E. C. Frey, B. M. W. Tsui, "Realistic CT Simulation using the 4D XCAT Phantom," Medical Physics, Vol. 35, No. 8, pp. 3800-3808, Aug. 2008. https://doi.org/10.1118/1.2955743