The Role of Regularization in Deformable Image Registration for Head and Neck Adaptive Radiotherapy

Deformable image registration provides a robust mathematical framework to quantify morphological changes that occur along the course of external beam radiotherapy treatments. As clinical reliability of deformable image registration is not always guaranteed, algorithm regularization is commonly introduced to prevent sharp discontinuities in the quantified deformation and achieve anatomically consistent results. In this work we analyzed the influence of regularization on two different registration methods, i.e. B-Splines and Log Domain Diffeomorphic Demons, implemented in an open-source platform. We retrospectively analyzed the simulation computed tomography (CTsim) and the corresponding re-planning computed tomography (CTrepl) scans in 30 head and neck cancer patients. First, we investigated the influence of regularization levels on hounsfield units (HU) information in 10 test patients for each considered method. Then, we compared the registration results of the open-source implementation at selected best performing regularization levels with a clinical commercial software on the remaining 20 patients in terms of mean volume overlap, surface and center of mass distances between manual outlines and propagated structures. The regularized B-Splines method was not statistically different from the commercial software. The tuning of the regularization parameters allowed open-source algorithms to achieve better results in deformable image registration for head and neck patients, with the additional benefit of a framework where regularization can be tuned on a patient specific basis.

[1]  Maria Francesca Spadea,et al.  Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy. , 2012, International journal of radiation oncology, biology, physics.

[2]  Marco Riboldi,et al.  Validation of deformable registration in adaptive radiation therapy with scale invariant feature transform , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[3]  Gregory Sharp,et al.  Analytic regularization for landmark-based image registration , 2012, Physics in medicine and biology.

[4]  S. Nill,et al.  A clinical concept for interfractional adaptive radiation therapy in the treatment of head and neck cancer. , 2012, International journal of radiation oncology, biology, physics.

[5]  Polina Golland,et al.  Ranking of stopping criteria for log domain diffeomorphic demons application in clinical radiation therapy , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Brian B. Avants,et al.  Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge , 2011, IEEE Transactions on Medical Imaging.

[7]  Todd McNutt,et al.  Volumetric change of selected organs at risk during IMRT for oropharyngeal cancer. , 2011, International journal of radiation oncology, biology, physics.

[8]  Lei Dong,et al.  Adaptive Radiation Therapy for Head and Neck Cancer—Can an Old Goal Evolve into a New Standard? , 2010, Journal of oncology.

[9]  Sasa Mutic,et al.  Technical note: DIRART--A software suite for deformable image registration and adaptive radiotherapy research. , 2011, Medical physics.

[10]  N Kandasamy,et al.  On developing B-spline registration algorithms for multi-core processors , 2010, Physics in medicine and biology.

[11]  Cai Grau,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 , 2010, Acta oncologica.

[12]  B. Gino Fallone,et al.  Assessment of a commercially available automatic deformable registration system , 2010, Journal of applied clinical medical physics.

[13]  Stephen Breen,et al.  Biomechanical-based image registration for head and neck radiation treatment , 2010, Medical Imaging.

[14]  K. Brock.  Results of a multi-institution deformable registration accuracy study (MIDRAS). , 2010, International journal of radiation oncology, biology, physics.

[15]  Tom Vercauteren,et al.  An ITK Implementation of the Symmetric Log-Domain Diffeomorphic Demons Algorithm , 2009, The Insight Journal.

[16]  Maria Francesca Spadea,et al.  Validation of an automatic contour propagation method for lung cancer 4D adaptive radiation therapy , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[17]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[18]  Weiguo Lu,et al.  Evaluation of geometric changes of parotid glands during head and neck cancer radiotherapy using daily MVCT and automatic deformable registration. , 2008, Radiotherapy and Oncology.

[19]  Benoît Macq,et al.  Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors. , 2008, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[20]  Quan Chen,et al.  Objective assessment of deformable image registration in radiotherapy: a multi-institution study. , 2007, Medical physics.

[21]  L. Dawson,et al.  Changes in Position and Size of Parotid Glands Assessed With Daily Cone-Beam CT During Image-Guided IMRT for Head and Neck Cancer: Implications for Dose Received , 2007 .

[22]  P. Keall,et al.  Computational challenges for image-guided radiation therapy: framework and current research. , 2007, Seminars in radiation oncology.

[23]  J. Piper,et al.  SU‐FF‐I‐68: Evaluation of An Intensity‐Based Free‐Form Deformable Registration Algorithm , 2007 .

[24]  Fabrice Heitz,et al.  Retrospective evaluation of a topology preserving non-rigid registration method , 2006, Medical Image Anal..

[25]  L. Xing,et al.  Image interpolation in 4D CT using a BSpline deformable registration model. , 2006, International journal of radiation oncology, biology, physics.

[26]  Radhe Mohan,et al.  Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/linear accelerator system. , 2004, International journal of radiation oncology, biology, physics.

[27]  J. Weaver,et al.  Multiresolution elastic image registration. , 1998, Medical physics.

[28]  Jean-Philippe Thirion,et al.  Non-rigid matching using demons , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[29]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[30]  P. Danielsson.  Euclidean distance mapping , 1980 .

[31]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .