Abstract
We evaluated the image registration accuracy achieved using two deformable registration algorithms when radiation-induced normal tissue changes were present between serial computed tomography (CT) scans. Two thoracic CT scans were collected for each of 24 patients who underwent radiation therapy (RT) treatment for lung cancer, eight of whom experienced radiologically evident normal tissue damage between pre- and post-RT scan acquisition. For each patient, 100 landmark point pairs were manually placed in anatomically corresponding locations between each pre- and post-RT scan. Each post-RT scan was then registered to the pre-RT scan using (1) the Plastimatch demons algorithm and (2) the Fraunhofer MEVIS algorithm. The registration accuracy for each scan pair was evaluated by comparing the distance between landmark points that were manually placed in the post-RT scans and points that were automatically mapped from pre- to post-RT scans using the displacement vector fields output by the two registration algorithms. For both algorithms, the registration accuracy was significantly decreased when normal tissue damage was present in the post-RT scan. Using the Plastimatch algorithm, registration accuracy was 2.4 mm, on average, in the absence of radiation-induced damage and 4.6 mm, on average, in the presence of damage. When the Fraunhofer MEVIS algorithm was instead used, registration errors decreased to 1.3 mm, on average, in the absence of damage and 2.5 mm, on average, when damage was present. This work demonstrated that the presence of lung tissue changes introduced following RT treatment for lung cancer can significantly decrease the registration accuracy achieved using deformable registration.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Murphy K, van Ginneken B, Reinhardt JM, Kabus S, Ding K, Deng X, Cao K, Du K, Christensen GE, Garcia V, Vercauteren T, Ayache N, Commowick O, Malandain G, Glocker B, Paragios N, Navab N, Gorbunova V, Sporring J, de Bruijne M, Han X, Heinrich MP, Schnabel JA, Jenkinson M, Lorenz C, Modat M, McClelland JR, Ourselin S, Muenzing SE, Viergever MA, De Nigris D, Collins DL, Arbel T, Peroni M, Li R, Sharp GC, Schmidt-Richberg A, Ehrhardt J, Werner R, Smeets D, Loeckx D, Song G, Tustison N, Avants B, Gee JC, Staring M, Klein S, Stoel BC, Urschler M, Werlberger M, Vandemeulebroucke J, Rit S, Sarrut D, Pluim JP: Evaluation of registration methods on thoracic CT: The EMPIRE10 challenge. IEEE Trans Med Imaging 30:1901–1920, 2011
Brock K: Results of a multi-institution deformable registration accuracy study (MIDRAS). Int J Radiat Oncol Biol Phys 76:583–596, 2010
Sharp G, Kandasamy N, Singh H, Folkert M: GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration. Phys Med Biol 52:5771–5783, 2007
Cunliffe A, Al-Hallaq H, Labby Z, Pelizzari C, Straus C, Sensakovic W, Ludwig M, Armato S: Lung texture in serial thoracic CT scans: assessment of change introduced by image registration. Med Phys 8:4679–4690, 2012
Palma DA, Van Sörnsen de Koste J, Verbakel WFAR, Senan S: A new approach to quantifying lung damage after stereotactic body radiation therapy. Acta Oncol 50:509–517, 2011
Rühaak J, Heldmann S, Kipshagen T, Fischer B: Highly accurate fast lung CT registration. Proc SPIE 8669:86690Y–86690Y–9, 2013
Deasy JO, Blanco AI, Clark VH: CERR: A computational environment for radiotherapy research. Med Phys 30:979–985, 2003
Cunliffe AR, Armato SG, Straus C, Malik R, Al-Hallaq HA: Lung texture in serial thoracic CT scans: correlation with radiologist-defined severity of acute changes following radiation therapy. Phys Med Biol 59: 5387–5398, 2014
Cunliffe AR, Armato SG, Fei XM, Tuohy RE, Al-Hallaq HA: Lung texture in serial thoracic CT scans: registration-based methods to compare anatomically matched regions. Med Phys 40: 061906–1–061906–9, 2013
Murphy K, van Ginneken B, Klein S, Staring M, de Hoop BJ, Viergever MA, Pluim JPW: Semi-automatic construction of reference standards for evaluation of image registration. Med Img Anal 15:71–84, 2011
Castillo R, Castillo E, Guerra R, Johnson VE, McPhail T, Garg AK, Guerrero T: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys Med Biol 54:1849–1870, 2009
Castillo R, Castillo E, Martinez J, Shenoy M, Guerrero T: Four-dimensional deformable image registration using trajectory modeling. Phys Med Biol 5:305–327, 2009
Movasas B, Raffin TA, Epstein AH, Link CJ: Pulmonary radiation injury. Chest 111:1061–1076, 1997
Jenkins P, Welsch A: Computed tomography appearance of early radiation injury to the lung: Correlation with clinical and dosimetric factors. Int J Radiat Oncol Biol Phys 54:329–339, 2002
Acknowledgments
Supported, in part, by NSF REU Award No. 1062909 and NIH Grant Nos. S10 RR021039, P30 CA14599, and T32 EB002103. Anonymized, compliant images were provided by The Human Imaging Research Office (HIRO), which is supported in part by pilot research funding provided by the Virginia and D.K. Ludwig Fund for Cancer Research through the Imaging Research Institute in the Biological Sciences Division of The University of Chicago.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was presented, in part, as an oral presentation at the SIIM 2014 Annual Meeting, Long Beach, CA (2014).
Rights and permissions
About this article
Cite this article
Cunliffe, A.R., White, B., Justusson, J. et al. Comparison of Two Deformable Registration Algorithms in the Presence of Radiologic Change Between Serial Lung CT Scans. J Digit Imaging 28, 755–760 (2015). https://doi.org/10.1007/s10278-015-9789-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10278-015-9789-1