Deformable image registration for adaptive radiation therapy of head and neck cancer: accuracy and precision in the presence of tumor changes.

PURPOSE To compare deformable image registration (DIR) accuracy and precision for normal and tumor tissues in head and neck cancer patients during the course of radiation therapy (RT). METHODS AND MATERIALS Thirteen patients with oropharyngeal tumors, who underwent submucosal implantation of small gold markers (average 6, range 4-10) around the tumor and were treated with RT were retrospectively selected. Two observers identified 15 anatomical features (landmarks) representative of normal tissues in the planning computed tomography (pCT) scan and in weekly cone beam CTs (CBCTs). Gold markers were digitally removed after semiautomatic identification in pCTs and CBCTs. Subsequently, landmarks and gold markers on pCT were propagated to CBCTs, using a b-spline-based DIR and, for comparison, rigid registration (RR). To account for observer variability, the pair-wise difference analysis of variance method was applied. DIR accuracy (systematic error) and precision (random error) for landmarks and gold markers were quantified. Time trend of the precisions for RR and DIR over the weekly CBCTs were evaluated. RESULTS DIR accuracies were submillimeter and similar for normal and tumor tissue. DIR precision (1 SD) on the other hand was significantly different (P<.01), with 2.2 mm vector length in normal tissue versus 3.3 mm in tumor tissue. No significant time trend in DIR precision was found for normal tissue, whereas in tumor, DIR precision was significantly (P<.009) degraded during the course of treatment by 0.21 mm/week. CONCLUSIONS DIR for tumor registration proved to be less precise than that for normal tissues due to limited contrast and complex non-elastic tumor response. Caution should therefore be exercised when applying DIR for tumor changes in adaptive procedures.

[1]  Quan Chen,et al.  Automatic re-contouring in 4D radiotherapy , 2006, Physics in medicine and biology.

[2]  Yaoqin Xie,et al.  Auto-propagation of contours for adaptive prostate radiation therapy , 2008, Physics in medicine and biology.

[3]  A. Hart,et al.  Assessment of analysis-of-variance-based methods to quantify the random variations of observers in medical imaging measurements: guidelines to the investigator. , 2004, Medical physics.

[4]  Connie M. Borror,et al.  Methods of Multivariate Analysis, 2nd Ed. , 2004 .

[5]  Sebastian Schafer,et al.  Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach. , 2011, Medical physics.

[6]  K. Brock,et al.  Demons deformable registration for CBCT-guided procedures in the head and neck: convergence and accuracy. , 2009, Medical physics.

[7]  Radhe Mohan,et al.  Multiple regions-of-interest analysis of setup uncertainties for head-and-neck cancer radiotherapy. , 2006, International journal of radiation oncology, biology, physics.

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

[9]  Paul Suetens,et al.  Nonrigid Image Registration Using Free-Form Deformations with a Local Rigidity Constraint , 2004, MICCAI.

[10]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[11]  Jan-Jakob Sonke,et al.  Evaluation of tumor shape variability in head-and-neck cancer patients over the course of radiation therapy using implanted gold markers. , 2012, International journal of radiation oncology, biology, physics.

[12]  Mathieu De Craene,et al.  Tumour delineation and cumulative dose computation in radiotherapy based on deformable registration of respiratory correlated CT images of lung cancer patients. , 2007, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[13]  T Kron,et al.  Is it sensible to "deform" dose? 3D experimental validation of dose-warping. , 2012, Medical physics.

[14]  Colin G Orton,et al.  Point/counterpoint: it is not appropriate to "deform" dose along with deformable image registration in adaptive radiotherapy. , 2012, Medical physics.

[15]  M. Staring,et al.  A rigidity penalty term for nonrigid registration. , 2007, Medical physics.

[16]  Ton J. Cleophas,et al.  Statistics Applied to Clinical Studies , 2012, Springer Netherlands.

[17]  Nicholas Ayache,et al.  The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.

[18]  G S Bauman,et al.  Tracking the dose distribution in radiation therapy by accounting for variable anatomy , 2004, Physics in medicine and biology.

[19]  Timothy E. Schultheiss,et al.  It is not appropriate to “deform” dose along with deformable image registration in adaptive radiotherapy: Point/Counterpoint , 2012 .

[20]  Jeffrey V Siebers,et al.  A distance to dose difference tool for estimating the required spatial accuracy of a displacement vector field. , 2010, Medical physics.

[21]  Rojano Kashani,et al.  Technical note: a physical phantom for assessment of accuracy of deformable alignment algorithms. , 2007, Medical physics.

[22]  Michael Unser,et al.  Fast parametric elastic image registration , 2003, IEEE Trans. Image Process..

[23]  A. C. Rencher.  Methods of multivariate analysis , 1995 .

[24]  Jan-Jakob Sonke,et al.  Setup uncertainties of anatomical sub-regions in head-and-neck cancer patients after offline CBCT guidance. , 2009, International journal of radiation oncology, biology, physics.

[25]  Sebastian Schafer,et al.  Extra-dimensional Demons: a method for incorporating missing tissue in deformable image registration. , 2012, Medical physics.

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

[27]  Alex Lukaj,et al.  Random positional variation among the skull, mandible, and cervical spine with treatment progression during head-and-neck radiotherapy. , 2009, International journal of radiation oncology, biology, physics.

[28]  H. Kooy,et al.  Automatic three-dimensional correlation of CT-CT, CT-MRI, and CT-SPECT using chamfer matching. , 1994, Medical physics.

[29]  Y. Nishimura,et al.  Analysis of interfractional set-up errors and intrafractional organ motions during IMRT for head and neck tumors to define an appropriate planning target volume (PTV)- and planning organs at risk volume (PRV)-margins. , 2006, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[30]  K. Brock,et al.  Accurate accumulation of dose for improved understanding of radiation effects in normal tissue. , 2010, International journal of radiation oncology, biology, physics.

[31]  Mariana Guerrero,et al.  Deformable planning CT to cone-beam CT image registration in head-and-neck cancer. , 2011, Medical physics.

[32]  Quan Chen,et al.  Objective assessment of deformable image registration in radiotherapy: A multi-institution study , 2008 .

[33]  M van Herk,et al.  Validation of deformable registration in head and neck cancer using analysis of variance. , 2012, Medical physics.