A Feasibility Study of Tumor Motion Estimate With Regional Deformable Registration Method for 4-Dimensional Radiation Therapy of Lung Cancer

This study aims to employ 4-dimensional computed tomography to quantify intrafractional tumor motion for patients with lung cancer to improve target localization in radiation therapy. A multistage regional deformable registration was implemented to calculate the excursion of gross tumor volume (GTV) during a breathing cycle. GTV was initially delineated on 0% phase of 4-dimensional computed tomography manually, and a subregion with 20 mm margin supplemented to GTV was generated with Eclipse treatment planning system (Varian Medical Systems, Palo Alto, California). The structures, together with the 4-dimensional computed tomography set, were exported into an in-house software, with which a 3-stage B-spline deformable registration was carried out to map the subregion and warp GTV contour to other breathing phases. The center of mass of the GTV was computed using the contours, and the tumor motion was appraised as the excursion of the center of mass between 0% phase and other phases. Application of the algorithm to the 10 patients showed that clinically satisfactory outcomes were achievable with a spatial accuracy around 2 mm for GTV contour propagation between adjacent phases and 3 mm between opposite phases. The tumor excursion was determined in the vast range of 1 mm through 1.6 cm, depending on the tumor location and tumor size. Compared to the traditional whole image-based registration, the regional method was found computationally a factor of 5 more efficient. The proposed technique has demonstrated its capability in extracting thoracic tumor motion and should find its application in 4-dimensional radiation therapy in the future to maximally utilize the available spatial–temporal information.

[1]  Vladimir Pekar,et al.  Assessment of a model-based deformable image registration approach for radiation therapy planning. , 2007, International journal of radiation oncology, biology, physics.

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

[3]  Daniel Y. Sze,et al.  Migration of implanted markers for image‐guided lung tumor stereotactic ablative radiotherapy , 2013, Journal of applied clinical medical physics.

[4]  Nikos Paragios,et al.  Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.

[5]  P. Keall,et al.  Four-dimensional IMRT treatment planning using a DMLC motion-tracking algorithm , 2009, Physics in medicine and biology.

[6]  T. Guerrero,et al.  Acquiring 4D thoracic CT scans using a multislice helical method. , 2004, Physics in medicine and biology.

[7]  J. Pouliot,et al.  A three-dimensional head-and-neck phantom for validation of multimodality deformable image registration for adaptive radiotherapy. , 2014, Medical physics.

[8]  Nzhde Agazaryan,et al.  An evaluation of gating window size, delivery method, and composite field dosimetry of respiratory-gated IMRT. , 2002, Medical physics.

[9]  Ruijiang Li,et al.  An initial study on the estimation of time-varying volumetric treatment images and 3D tumor localization from single MV cine EPID images. , 2014, Medical physics.

[10]  E. Yorke,et al.  Respiratory gating for liver tumors: Use in dose escalation , 2003 .

[11]  Geoffrey G. Zhang,et al.  Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method. , 2004, Physics in medicine and biology.

[12]  Christoph Bert,et al.  4D optimization of scanned ion beam tracking therapy for moving tumors , 2014, Physics in medicine and biology.

[13]  G J Kutcher,et al.  Deep inspiration breath-hold technique for lung tumors: the potential value of target immobilization and reduced lung density in dose escalation. , 1999, International journal of radiation oncology, biology, physics.

[14]  Pietro Cerveri,et al.  Surrogate-driven deformable motion model for organ motion tracking in particle radiation therapy , 2015, Physics in medicine and biology.

[15]  Martin J Murphy,et al.  Issues in respiratory motion compensation during external-beam radiotherapy. , 2002, International journal of radiation oncology, biology, physics.

[16]  B. Heijmen,et al.  Geometrical uncertainties, radiotherapy planning margins, and the ICRU-62 report. , 2002, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[17]  L. Xing,et al.  Tissue feature-based and segmented deformable image registration for improved modeling of shear movement of lungs. , 2009, International journal of radiation oncology, biology, physics.

[18]  David Sarrut,et al.  Multi-dimensional respiratory motion tracking from markerless optical surface imaging based on deformable mesh registration , 2012, Physics in medicine and biology.

[19]  E. Oermann,et al.  CyberKnife with Tumor Tracking: An Effective Treatment for High-Risk Surgical Patients with Stage I Non-Small Cell Lung Cancer , 2012, Front. Oncol..

[20]  Jonathan G. Li,et al.  Inverse planning incorporating organ motion. , 2000 .

[21]  Yaoqin Xie,et al.  Image-based modeling of tumor shrinkage in head and neck radiation therapy. , 2010, Medical physics.

[22]  Yaoqin Xie,et al.  Deformable image registration of liver with consideration of lung sliding motion. , 2011, Medical physics.

[23]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[24]  Steve B Jiang,et al.  Synchronized moving aperture radiation therapy (SMART): average tumour trajectory for lung patients. , 2003, Physics in medicine and biology.

[25]  Mitsuhiro Nakamura,et al.  Guidelines for respiratory motion management in radiation therapy , 2012, Journal of radiation research.

[26]  David Sarrut,et al.  Tumor tracking method based on a deformable 4D CT breathing motion model driven by an external surface surrogate. , 2014, International journal of radiation oncology, biology, physics.

[27]  Jae Hyuk Seo,et al.  Reconstitution of internal target volumes by combining four-dimensional computed tomography and a modified slow computed tomography scan in stereotactic body radiotherapy planning for lung cancer , 2014, Radiation oncology.

[28]  C C Ling,et al.  The deep inspiration breath-hold technique in the treatment of inoperable non-small-cell lung cancer. , 2000, International journal of radiation oncology, biology, physics.

[29]  John H. Lewis,et al.  Registration of clinical volumes to beams-eye-view images for real-time tracking. , 2014, Medical physics.

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

[31]  David R. Haynor,et al.  Nonrigid multimodality image registration , 2001, SPIE Medical Imaging.

[32]  Guy Marchal,et al.  Automated multi-modality image registration based on information theory , 1995 .

[33]  R. Mohan,et al.  Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. , 2003, Physics in medicine and biology.

[34]  George T. Y. Chen,et al.  Four-dimensional image-based treatment planning: Target volume segmentation and dose calculation in the presence of respiratory motion. , 2005, International journal of radiation oncology, biology, physics.

[35]  Volumetric tracking tool using four-dimensional CT for image guided-radiation therapy , 2008, Radiological physics and technology.

[36]  Paul E Kinahan,et al.  Challenges and opportunities in patient-specific, motion-managed and PET/CT-guided radiation therapy of lung cancer: review and perspective , 2012, Clinical and Translational Medicine.

[37]  F. Bookstein.  Thin-plate splines and decomposition of deformation , 1989 .