A feature-based affine registration method for capturing background lung tissue deformation for ground glass nodule tracking

References

• Ashburner J. 2007. A fast diffeomorphic image registration algorithm. NeuroImage. 38(1):95–113. doi:10.1016/j.neuroimage.2007.07.007.
   Google Scholar
• Balci SK, Golland P, Shenton M, Wells WM 2007. Free-form B-spline deformation model for groupwise registration. In: Proc. Medical Image Computing and Computer-assisted Intervention. Brisbane, Australia: Statistical Registration Workshop. p. 23–30.
   Google Scholar
• Bjerhammar A. 1928. Rectangular reciprocal matrices, with special reference to geodetic calculations. Bulletin Géodésique (1946-1975). 20(1):188–220. doi:10.1007/BF02526278.
   Google Scholar
• Brock KK. 2010. Results of a Multi-Institution Deformable Registration Accuracy Study (MIDRAS). Int J Radiat Oncol Biol Phys. 76(2):583–596. doi:10.1016/j.ijrobp.2009.06.031.
   Google Scholar
• Cahill ND 2012. Motion coherent image registration and demons: practical handling of deformation boundaries. In: Proc. SPIE Medical Imaging 2012. CA, USA: Image Processing; vol. 8314. p. 83141R.
   Google Scholar
• Cao Y, Miller M, Winslow RL, Younes L. 2005. Large deformation diffeomorphic metric mapping of vector fields. IEEE Trans Med Imaging. 24(9):1216–1230. doi:10.1109/TMI.2005.853923.
   Google Scholar
• Cormen TH, Stein C, Rivest RL, Leiserson CE. 2001. Introduction to algorithms. 2nd. MA, USA: McGraw-Hill Higher Education.
   Google Scholar
• El-Baz A, Beache GM, Gimel’Farb G, Suzuki K, Okada K, Elnakib A, Soliman A, Abdollahi B. 2013. Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Int J Biomed Imaging. 2013.
   Google Scholar
• Fischbach F, Knollmann F, Griesshaber V, Freund T, Akkol E, Felix R. 2003. Detection of pulmonary nodules by multislice computed tomography: improved detection rate with reduced slice thickness. Eur Radiol. 13(10):2378–2383. doi:10.1007/s00330-003-1915-7.
   Google Scholar
• Gill PE, Murray W, Wright MH. 1981. Practical optimization. London, UK: Academic press.
   Google Scholar
• Godoy MCB, Naidich DP. 2012. Overview and strategic management of subsolid pulmonary nodules. J Thorac Imaging. 27(4):240–248. doi:10.1097/RTI.0b013e31825d515b.
   Google Scholar
• Hansell DM, Bankier A, MacMahon H, McLoud TC, Müller NL, Remy J. 2008. Fleischner society: glossary of terms for thoracic imaging. Radiology. 246(3):697–722. doi:10.1148/radiol.2462070712.
   Google Scholar
• Henschke CI, McCauley DI, Yankelevitz DF, Naidich DP, McGuinness G, Miettinen OS, Libby DM, Pasmantier MW, Koizumi J, Altorki NK, et al. 1999. Early lung cancer action project: overall design and findings from baseline screening. Lancet. 354(9173):99–105. doi:10.1016/S0140-6736(99)06093-6.
   Google Scholar
• Henschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D, Miettinen OS. 2002. CT screening for lung cancer. American Journal of Roentgenology. 178(5):1053–1057. doi:10.2214/ajr.178.5.1781053.
   Google Scholar
• Jacobs C, Van Rikxoort E, Twellmann T, Scholten E, de Jong P, Kuhnigk J, Oudkerk M, de Koning H, Prokop M, Schaefer-Prokop C, et al. 2014. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal. 18(2):374–384. doi:10.1016/j.media.2013.12.001.
   Google Scholar
• Kakinuma R, Ashizawa K, Kuriyama K, Fukushima A, Ishikawa H, Kamiya H, Koizumi N, Maruyama Y, Minami K, Nitta N, et al. 2012. Measurement of focal ground-glass opacity diameters on CT images. interobserver agreement in regard to identifying increases in the size of ground-glass opacities. Acad Radiol. 19(4):389–394. doi:10.1016/j.acra.2011.12.002.
   Google Scholar
• Kim H, Park CM, Lee SM, Lee HJ. 2013. Measurement variability of volume and mass in nodules with a solid portion less than or equal to 5 mm. Radiology. 269(2):585–593. doi:10.1148/radiol.13121849.
   Google Scholar
• Ko JP, Berman EJ, Kaur M, Babb JS, Bomsztyk E, Greenberg A, Naidich DP, Rusinek H. 2012. Pulmonary nodules: Growth rate assessment in patients by using serial CT and three-dimensional volumetry. Radiology. 262(2):662–671. doi:10.1148/radiol.11100878.
   Google Scholar
• Lee SM, Park CM, Goo JM, Lee CH, Lee HJ, Kim KG, Kang MJ, Lee IS. 2010. Transient part-solid nodules detected at screening thin-section CT for lung cancer: comparison with persistent part-solid nodules. Radiology. 255(1):242–251. doi:10.1148/radiol.09090547.
   Google Scholar
• Lorenzi M, Ayache N, Frisoni GB, Pennec X. 2013. LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm. NeuroImage. 81:470–483. doi:10.1016/j.neuroimage.2013.04.114.
   Google Scholar
• Maurer CR, Qi R, Raghavan V. 2003. A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Pattern Anal Mach Intell. 25(2):265–270. doi:10.1109/TPAMI.2003.1177156.
   Google Scholar
• Meshgi K, Ishii S 2015. Expanding histogram of colors with gridding to improve tracking accuracy. In: Proc. IEEE International Conference on Machine Vision Applications, Tokyo, Japan. p. 475–479.
   Google Scholar
• Niethammer M, Hart GL, Pace DF, Vespa PM, Irimia A, Van Horn JD, Aylward SR 2011. Geometric metamorphosis. In: Proc. Medical Image Computing and Computer-assisted intervention MICCAI Lecture Notes in Computer Science. Toronto, Canada. vol. 6892. p. 639–646.
   Google Scholar
• Rajpoot K, Grau V, Noble JA 2009. Local-phase based 3-D boundary detection using monogenic signal and its application to real-time 3-D echocardiography images. In: Proc. IEEE International Symposium on Biomedical Imaging: From Nano to Macro. MA, USA. 2009. p. 783–786.
   Google Scholar
• Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ. 1999. Nonrigid registration using free-form deformations: application to breast mr images. Medical Imaging, IEEE Transactions On. 18:712–721. doi:10.1109/42.796284.
   Google Scholar
• Song G, Tustison N, Avants B, Gee JC. 2010. Lung CT image registration using diffeomorphic transformation models. In: Medical image analysis for the clinic: a grand challenge. p. 23–32.
   Google Scholar
• Sorkine-Hornung O, Rabinovich M 2017. Least-squares rigid motion using svd. ETH Zurich. Report no.:.
   Google Scholar
• Staring M, Bakker ME, Stolk J, Shamonin DP, Reiber JHC, Stoel BC. 2014. Towards local progression estimation of pulmonary emphysema using CT. Med Phys. 41(2):021905. doi:10.1118/1.4851535.
   Google Scholar
• Staring M, Pluim JPW, De Hoop B, Klein S, van Ginneken B, Gietema H, Nossent G, Schaefer-Prokop C, van de Vorst S, Prokop M. 2009. Image subtraction facilitates assessment of volume and density change in ground-glass opacities in chest CT. Invest Radiol. 44(2):61–66. doi:10.1097/RLI.0b013e318197fcb7.
   Google Scholar
• Tanner C, Schnabel J, Chung D, Clarkson M, Rueckert D, Hill D, Hawkes D 2000. Volume and shape preservation of enhancing lesions when applying non-rigid registration to a time series of contrast enhancing MR breast images. In: Proc. Medical Image Computing and Computer Assisted Interventions MICCAI - Lecture Notes in Computer Science, Pittsburgh, PA, USA; vol. 1935. p. 327–337.
   Google Scholar
• Thirion JP 1995. Fast non-rigid matching of 3D medical images. INRIA. Report no
   Google Scholar
• Tustison NJ, Cook TS, Song G, Gee JC. 2011. Pulmonary kinematics from image data: A review. Acad Radiol. 18(4):402–417. doi:10.1016/j.acra.2010.10.019.
   Google Scholar
• Murphy K, van Ginneken B, Reinhardt JM, Kabus S, Ding K, Deng X, Cao K, Du K, Christensen GE, Garcia V, Vercauteren T and et al. 2011. Evaluation of registration methods on thoracic CT: The EMPIRE10 challenge. IEEE Trans Med Imaging. 30(11):1901–1920. doi:10.1109/TMI.2011.2158349.
   Google Scholar
• Werner R, Schmidt-Richberg A, Handels H, Ehrhardt J. 2014. Estimation of lung motion fields in 4D CT data by variational non-linear intensity-based registration: a comparison and evaluation study. Phys Med Biol. 59(15):4247–4260. doi:10.1088/0031-9155/59/15/4247.
   Google Scholar
• Wormanns D, Kohl G, Klotz E, Marheine A, Beyer F, Heindel W, Diederich S. 2004. Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur Radiol. 14(1):86–92. doi:10.1007/s00330-003-2132-0.
   Google Scholar
• Zhao B, Yankelevitz D, Reeves A, Henschke C. 1999. Two-dimensional multi-criterion segmentation of pulmonary nodules on helical CT images. Med Phys. 26(6):889–895. doi:10.1118/1.598605.
   Google Scholar
• Zheng Y, Kambhamettu C, Bauer T, Steiner K 2009. Accurate estimation of pulmonary nodule’s growth rate in CT images with nonrigid registration and precise nodule detection and segmentation. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Miami, FL, USA. p. 101–108.
   Google Scholar
• Zheng Y, Steiner K, Bauer T, Yu J, Shen D, Kambhamettu C 2007. Lung nodule growth analysis from 3D CT data with a coupled segmentation and registration framework. In: Proc. IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil. p. 1–8.
   Google Scholar