Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT Studies

Tumor shrinkage occurs in many patients undergoing radiotherapy for head-and-neck (H&N) cancer. However, one-to-one correspondence is not always available between voxels of two image sets. This makes intensity-based deformable registration difficult and inaccurate. In this paper, we describe a novel method to increase the performance of the registration in presence of tumor shrinkage. The method combines an image modification procedure and a fast symmetric Demons algorithm to register CT images acquired at planning and posttreatment fractions. The image modification procedure modifies the image intensities of the primary tumor by calculating tumor cell survival rate using the linear quadratic (LQ) model according to the dose delivered to the tumor. A scale operation is used to deal with uncertainties in biological parameters. The method was tested in 10 patients with nasopharyngeal cancer (NPC). Registration accuracy was improved compared with that achieved using the symmetric Demons algorithm. The average Dice similarity coefficient (DSC) increased by 21%. This novel method is suitable for H&N adaptive radiation therapy.

[1]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[2]  Benoît Macq,et al.  Evaluation of the radiobiological impact of anatomic modifications during radiation therapy for head and neck cancer: can we simply summate the dose? , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

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

[4]  Radhe Mohan,et al.  A deformable image registration method to handle distended rectums in prostate cancer radiotherapy. , 2006, Medical physics.

[5]  Joseph O Deasy,et al.  Deformable registration of abdominal kilovoltage treatment planning CT and tomotherapy daily megavoltage CT for treatment adaptation. , 2009, Medical physics.

[6]  G C Sharp,et al.  GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration , 2007, Physics in medicine and biology.

[7]  Vincent Gregoire,et al.  Molecular imaging-based dose painting: a novel paradigm for radiation therapy prescription. , 2011, Seminars in radiation oncology.

[8]  X Allen Li,et al.  Automated registration of large deformations for adaptive radiation therapy of prostate cancer. , 2008, Medical physics.

[9]  R. Jena,et al.  Correlation of a hypoxia based tumor control model with observed local control rates in nasopharyngeal carcinoma treated with chemoradiotherapy. , 2010, Medical physics.

[10]  Qiuwen Wu,et al.  Adaptive replanning strategies accounting for shrinkage in head and neck IMRT. , 2009, International journal of radiation oncology, biology, physics.

[11]  Sarang Joshi,et al.  Large deformation three-dimensional image registration in image-guided radiation therapy , 2005, Physics in medicine and biology.

[12]  Radhe Mohan,et al.  Implementation and validation of a three-dimensional deformable registration algorithm for targeted prostate cancer radiotherapy. , 2004, International journal of radiation oncology, biology, physics.

[13]  S Webb,et al.  Optimum parameters in a model for tumour control probability including interpatient heterogeneity. , 1995, Physics in medicine and biology.

[14]  J F Fowler,et al.  Is there an optimum overall time for head and neck radiotherapy? A review, with new modelling. , 2007, Clinical oncology (Royal College of Radiologists (Great Britain)).

[15]  R. Castillo,et al.  A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets , 2009, Physics in medicine and biology.

[16]  Ergun Ahunbay,et al.  Automated registration of large deformations for adaptive radiation therapy of prostate cancer. , 2009 .

[17]  A. Beddoe,et al.  The effects of delays in radiotherapy treatment on tumour control. , 2003, Physics in medicine and biology.

[18]  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.

[19]  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.

[20]  J. Fowler.  The linear-quadratic formula and progress in fractionated radiotherapy. , 1989, The British journal of radiology.

[21]  Ping Xia,et al.  Repeat CT imaging and replanning during the course of IMRT for head-and-neck cancer. , 2006, International journal of radiation oncology, biology, physics.

[22]  Joe Y. Chang,et al.  Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy , 2005, Physics in medicine and biology.

[23]  F Zerrin Yetkin,et al.  Hypoxia imaging in brain tumors. , 2002, Neuroimaging clinics of North America.

[24]  Xavier Geets,et al.  Adaptive functional image-guided IMRT in pharyngo-laryngeal squamous cell carcinoma: is the gain in dose distribution worth the effort? , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[25]  David Sarrut,et al.  Deformable registration for image-guided radiation therapy. , 2006, Zeitschrift fur medizinische Physik.

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

[27]  E. J. Gómez,et al.  Methodology for registration of distended rectums in pelvic CT studies. , 2012, Medical physics.