Validation of a deformable image registration technique for cone beam CT-based dose verification.

PURPOSE As radiation therapy evolves toward more adaptive techniques, image guidance plays an increasingly important role, not only in patient setup but also in monitoring the delivered dose and adapting the treatment to patient changes. This study aimed to validate a method for evaluation of delivered intensity modulated radiotherapy (IMRT) dose based on multimodal deformable image registration (dir) for prostate treatments. METHODS A pelvic phantom was scanned with CT and cone-beam computed tomography (CBCT). Both images were digitally deformed using two realistic patient-based deformation fields. The original CT was then registered to the deformed CBCT resulting in a secondary deformed CT. The registration quality was assessed as the ability of the dir method to recover the artificially induced deformations. The primary and secondary deformed CT images as well as vector fields were compared to evaluate the efficacy of the registration method and it's suitability to be used for dose calculation. plastimatch, a free and open source software was used for deformable image registration. A B-spline algorithm with optimized parameters was used to achieve the best registration quality. Geometric image evaluation was performed through voxel-based Hounsfield unit (HU) and vector field comparison. For dosimetric evaluation, IMRT treatment plans were created and optimized on the original CT image and recomputed on the two warped images to be compared. The dose volume histograms were compared for the warped structures that were identical in both warped images. This procedure was repeated for the phantom with full, half full, and empty bladder. RESULTS The results indicated mean HU differences of up to 120 between registered and ground-truth deformed CT images. However, when the CBCT intensities were calibrated using a region of interest (ROI)-based calibration curve, these differences were reduced by up to 60%. Similarly, the mean differences in average vector field lengths decreased from 10.1 to 2.5 mm when CBCT was calibrated prior to registration. The results showed no dependence on the level of bladder filling. In comparison with the dose calculated on the primary deformed CT, differences in mean dose averaged over all organs were 0.2% and 3.9% for dose calculated on the secondary deformed CT with and without CBCT calibration, respectively, and 0.5% for dose calculated directly on the calibrated CBCT, for the full-bladder scenario. Gamma analysis for the distance to agreement of 2 mm and 2% of prescribed dose indicated a pass rate of 100% for both cases involving calibrated CBCT and on average 86% without CBCT calibration. CONCLUSIONS Using deformable registration on the planning CT images to evaluate the IMRT dose based on daily CBCTs was found feasible. The proposed method will provide an accurate dose distribution using planning CT and pretreatment CBCT data, avoiding the additional uncertainties introduced by CBCT inhomogeneity and artifacts. This is a necessary initial step toward future image-guided adaptive radiotherapy of the prostate.

[1]  Wolfgang Birkfellner,et al.  Image quality and stability of image-guided radiotherapy (IGRT) devices: A comparative study. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[2]  Jiazhou Wang,et al.  Use of kilovoltage X-ray volume imaging in patient dose calculation for head-and-neck and partial brain radiation therapy , 2010, Radiation oncology.

[3]  J H Siewerdsen,et al.  Cone-beam computed tomography with a flat-panel imager: initial performance characterization. , 2000, Medical physics.

[4]  Fang-Fang Yin,et al.  Feasibility study of a synchronized-moving-grid (SMOG) system to improve image quality in cone-beam computed tomography (CBCT). , 2012, Medical physics.

[5]  Weiguo Lu,et al.  Deformable registration of the planning image (kVCT) and the daily images (MVCT) for adaptive radiation therapy , 2006, Physics in medicine and biology.

[6]  J. Cheng,et al.  Practically acquired and modified cone-beam computed tomography images for accurate dose calculation in head and neck cancer , 2011, Strahlentherapie und Onkologie.

[7]  J. Debus,et al.  Correction of patient positioning errors based on in-line cone beam CTs: clinical implementation and first experiences , 2005, Radiation oncology.

[8]  Boyd McCurdy,et al.  Cone beam computerized tomography: the effect of calibration of the Hounsfield unit number to electron density on dose calculation accuracy for adaptive radiation therapy , 2009, Physics in medicine and biology.

[9]  Ning Wen,et al.  Combining scatter reduction and correction to improve image quality in cone-beam computed tomography (CBCT). , 2010, Medical physics.

[10]  Eduard Schreibmann,et al.  Mapping Electron Density Distribution from Planning CT to Cone-Beam CT (CBCT): a Novel Strategy for Accurate Dose Calculation Based on CBCT , 2005 .

[11]  Steve B. Jiang,et al.  Deformable image registration of CT and truncated cone-beam CT for adaptive radiation therapy , 2013, Physics in medicine and biology.

[12]  Markus Stock,et al.  Feasibility of CBCT-based dose calculation: comparative analysis of HU adjustment techniques. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[13]  Lei Xing,et al.  Evaluation of on-board kV cone beam CT (CBCT)-based dose calculation , 2007, Physics in medicine and biology.

[14]  Maria Thor,et al.  Deformable image registration for contour propagation from CT to cone-beam CT scans in radiotherapy of prostate cancer , 2011, Acta oncologica.

[15]  Uwe Oelfke,et al.  Enhancement of image quality with a fast iterative scatter and beam hardening correction method for kV CBCT. , 2009, Zeitschrift fur medizinische Physik.

[16]  David A Jaffray,et al.  Online planning and delivery technique for radiotherapy of spinal metastases using cone-beam CT: image quality and system performance. , 2007, International journal of radiation oncology, biology, physics.

[17]  Huaiqun Guan,et al.  Dose calculation accuracy using cone-beam CT (CBCT) for pelvic adaptive radiotherapy , 2009, Physics in medicine and biology.

[18]  T. Solberg,et al.  Localization accuracy and immobilization effectiveness of a stereotactic body frame for a variety of treatment sites. , 2013, International journal of radiation oncology, biology, physics.

[19]  Matthias Guckenberger,et al.  Investigation of the usability of conebeam CT data sets for dose calculation , 2008, Radiation oncology.

[20]  D. Low,et al.  A technique for the quantitative evaluation of dose distributions. , 1998, Medical physics.

[21]  W. Tomé,et al.  Dose calculation on kV cone beam CT images: an investigation of the Hu-density conversion stability and dose accuracy using the site-specific calibration. , 2010, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[22]  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.

[23]  M. Oldham,et al.  Cone-beam-CT guided radiation therapy: technical implementation. , 2005, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[24]  Xiang Li,et al.  Simultaneous reduction of radiation dose and scatter for CBCT by using collimators. , 2013, Medical physics.

[25]  N. Kadoya,et al.  Evaluation of on-board kV cone beam computed tomography-based dose calculation with deformable image registration using Hounsfield unit modifications. , 2014, International journal of radiation oncology, biology, physics.

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

[27]  Gregory C Sharp,et al.  Numerical solutions of the γ-index in two and three dimensions , 2012, Physics in medicine and biology.

[28]  Wiro J Niessen,et al.  Feasibility of multimodal deformable registration for head and neck tumor treatment planning. , 2014, International journal of radiation oncology, biology, physics.

[29]  Jinkoo Kim,et al.  A novel approach for establishing benchmark CBCT/CT deformable image registrations in prostate cancer radiotherapy. , 2013, Physics in medicine and biology.

[30]  Friedlieb Lorenz,et al.  A new strategy for online adaptive prostate radiotherapy based on cone-beam CT. , 2009, Zeitschrift fur medizinische Physik.

[31]  Sébastien Ourselin,et al.  Toward adaptive radiotherapy for head and neck patients: Feasibility study on using CT-to-CBCT deformable registration for "dose of the day" calculations. , 2014, Medical physics.

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

[33]  K. Ogawa,et al.  Dose calculation with a cone beam CT image in image-guided radiation therapy , 2012, Radiological Physics and Technology.

[34]  Kari Tanderup,et al.  Acceleration and validation of optical flow based deformable registration for image-guided radiotherapy , 2008, Acta oncologica.

[35]  H. Vorwerk,et al.  Target volume coverage and dose to organs at risk in prostate cancer patients , 2014, Strahlentherapie und Onkologie.

[36]  Eduard Schreibmann,et al.  Quantitative evaluation of a cone‐beam computed tomography–planning computed tomography deformable image registration method for adaptive radiation therapy , 2007, Journal of applied clinical medical physics.

[37]  L. Xing,et al.  Retrospective IMRT dose reconstruction based on cone-beam CT and MLC log-file. , 2008, International journal of radiation oncology, biology, physics.

[38]  Luo Ouyang,et al.  A moving blocker system for cone-beam computed tomography scatter correction. , 2013, Medical physics.