Abstract
Accurate dose predication is critical to spinal stereotactic body radiation therapy (SBRT). It enables radiation oncologists and planners to design treatment plans that maximally protect spinal cord while effectively controlling surrounding tumors. Spinal cord dose distribution is primarily affected by the shapes of tumor boundaries near the organ. In this work, we estimate such boundary effects and predict dose distribution by exploring an active optical flow model (AOFM). To establish AOFM, we collect a sequence of dose sub-images and tumor contours near spinal cords from a database of clinically accepted spine SBRT plans. The data are classified into five groups according to the tumor location in relation to the spinal cords. In each group, we randomly choose a dose sub-image as the reference and register all other dose images to the reference using an optical flow method. AOFM is then constructed by importing optical flow vectors and dose values into the principal component analysis. To develop the predictive model for a group, we also build active shape model (ASM) of tumor contours near the spinal cords. The correlation between ASM and AOFM is estimated via the multiple regression model. When predicting dose distribution of a new case, the group was first determined based on the case’s tumor contour. Then the corresponding model for the group is used to map from the ASM space to the AOFM space. Finally, the parameters in the AOFM space are used to estimate dose distribution. This method was validated on 30 SBRT plans. Analysis of dose-volume histograms revealed that at the important 2 % volume mark, the dose difference between prediction and clinical plan is less than \(4\,\%\). These results suggest that the AOFM-based approach is a promising tool for predicting accurate spinal cord dose in clinical practice.
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Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. Proc. ECCV 3024, 25–36 (2004)
Cootes, T., Cooper, D., Taylor, C., Graham, J.: Active shape models—their training and application. CVIU 61, 38–59 (1995)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE PAMI 23, 681–685 (2001)
Heimann, T., Meinzer, H.: Statistical shape models for 3d medical image segmentation: a review. Med. Image Anal. 13(4), 543–563 (2009)
Holt, T., Hoskin, P., Maranzano, E., et al.: Malignant epidural spinal cord compression: the role of external beam radiotherapy. Curr. Opin. Supportive Palliat. Care 6(1), 103–108 (2012)
Horkaew, P., Yang, G.: Optimal deformable surface models for 3d medical image analysis. IPMI 2732, 13–24 (2003)
Ibanez, L., Schroeder, W., Ng, L., Cates, J.: The ITK Software Guide. Kitware, Clifton Park (2005)
Neter, J., Kutner, M., Nachtsheim, C., Wasserman, W.: Applied Linear Regression Models. Irwin, Homwood (1996)
Rueckert, D., Frangi, A., Schnabel, J.: Automatic construction of 3-d statistical deformation models of the brain using nonrigid registration. IEEE TMI 22, 1014–1025 (2003)
Yasushi, H., Kataoka, M., Senba, T., et al.: Vertebral metastases with high risk of symptomatic malignant spinal cord compression. Jpn. J. Clin. Oncol. 39(7), 431–434 (2009)
Yuan, L., Ge, Y., Lee, W., Yin, F., Kirpatrick, J., Wu, Q.: Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans. Med. Phys. 39(11), 6868–6878 (2012)
Zhang, X., Li, X., Quan, E., Li, Y.: A methodology for automatic intensity-modulated radiation treatment planning for lung cancer. Phys. Med. Biol. 56(13), 3873–3893 (2011)
Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. IJCV 13(2), 119–152 (1994)
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Liu, J., Jackie Wu, Q., Yin, FF., Kirkpatrick, J.P., Cabrera, A., Ge, Y. (2015). An Active Optical Flow Model for Dose Prediction in Spinal SBRT Plans. In: Yao, J., Glocker, B., Klinder, T., Li, S. (eds) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-14148-0_3
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DOI: https://doi.org/10.1007/978-3-319-14148-0_3
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