Phantom based evaluation of CT to CBCT image registration for proton therapy dose recalculation

The ability to perform dose recalculation on the anatomy of the day is important in the context of adaptive proton therapy. The objective of this study was to investigate the use of deformable image registration (DIR) and cone beam CT (CBCT) imaging to generate the daily stopping power distribution of the patient. We investigated the deformation of the planning CT scan (pCT) onto daily CBCT images to generate a virtual CT (vCT) using a deformable phantom designed for the head and neck (H & N) region. The phantom was imaged at a planning CT scanner in planning configuration, yielding a pCT and in deformed, treatment day configuration, yielding a reference CT (refCT). The treatment day configuration was additionally scanned at a CBCT scanner. A Morphons DIR algorithm was used to generate a vCT. The accuracy of the vCT was evaluated by comparison to the refCT in terms of corresponding features as identified by an adaptive scale invariant feature transform (aSIFT) algorithm. Additionally, the vCT CT numbers were compared to those of the refCT using both profiles and regions of interest and the volumes and overlap (DICE coefficients) of various phantom structures were compared. The water equivalent thickness (WET) of the vCT, refCT and pCT were also compared to evaluate proton range differences. Proton dose distributions from the same initial fluence were calculated on the refCT, vCT and pCT and compared in terms of proton range. The method was tested on a clinical dataset using a replanning CT scan acquired close in time to a CBCT scan as reference using the WET evaluation. Results from the aSIFT investigation suggest a deformation accuracy of 2-3 mm. The use of the Morphon algorithm did not distort CT number intensity in uniform regions and WET differences between vCT and refCT were of the order of 2% of the proton range. This result was confirmed by proton dose calculations. The patient results were consistent with phantom observations. In conclusion, our phantom study suggests the vCT approach is adequate for proton dose recalculation on the basis of CBCT imaging.

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

[2]  Leszek Wojnar,et al.  Image Analysis , 1998 .

[3]  Joseph O Deasy,et al.  CERR: a computational environment for radiotherapy research. , 2003, Medical physics.

[4]  Marco Riboldi,et al.  Quantification of organ motion based on an adaptive image-based scale invariant feature method. , 2013, Medical physics.

[5]  Harald Paganetti,et al.  Relative biological effectiveness (RBE) values for proton beam therapy. , 2002, International journal of radiation oncology, biology, physics.

[6]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Holly Ning,et al.  Comparison of intensity-modulated radiotherapy, adaptive radiotherapy, proton radiotherapy, and adaptive proton radiotherapy for treatment of locally advanced head and neck cancer. , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[8]  Ghassan Hamarneh,et al.  n -SIFT: n -Dimensional Scale Invariant Feature Transform , 2009, IEEE Trans. Image Process..

[9]  Frank Verhaegen,et al.  A fast three-dimensional gamma evaluation using a GPU utilizing texture memory for on-the-fly interpolations. , 2011, Medical physics.

[10]  Michael Fenech,et al.  Mitigating the risk of radiation-induced cancers: limitations and paradigms in drug development , 2014, Journal of radiological protection : official journal of the Society for Radiological Protection.

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

[12]  H. Paganetti.  Range uncertainties in proton therapy and the role of Monte Carlo simulations , 2012, Physics in medicine and biology.

[13]  Hans Knutsson,et al.  Non-rigid Registration Using Morphons , 2005, SCIA.

[14]  Hans Knutsson,et al.  Morphons: Paint on Priors and Elastic Canvas for Segmentation and Registration , 2005, SCIA.

[15]  Jan J Wilkens,et al.  Advanced treatment planning methods for efficient radiation therapy with laser accelerated proton and ion beams. , 2010, Medical physics.

[16]  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.

[17]  Di Yan,et al.  Automatic delineation of on-line head-and-neck computed tomography images: toward on-line adaptive radiotherapy. , 2007, International journal of radiation oncology, biology, physics.

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

[19]  Hanne M Kooy,et al.  Dose uncertainties in IMPT for oropharyngeal cancer in the presence of anatomical, range, and setup errors. , 2013, International journal of radiation oncology, biology, physics.

[20]  Gregory C Sharp,et al.  Scale invariant feature transform in adaptive radiation therapy: a tool for deformable image registration assessment and re-planning indication , 2013, Physics in medicine and biology.

[21]  Linghong Zhou,et al.  CT to cone-beam CT deformable registration with simultaneous intensity correction. , 2012, Physics in medicine and biology.

[22]  A. Trofimov,et al.  Interfractional variations in the setup of pelvic bony anatomy and soft tissue, and their implications on the delivery of proton therapy for localized prostate cancer. , 2011, International journal of radiation oncology, biology, physics.

[23]  S. Leung,et al.  Prospective randomized study of intensity-modulated radiotherapy on salivary gland function in early-stage nasopharyngeal carcinoma patients. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[24]  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.

[25]  Sunkil Park,et al.  On-board CBCT/CBDT for Image-guided Proton Therapy: Initial Performance Evaluation , 2009 .

[26]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[27]  Haihua Yang,et al.  Clinical study of the necessity of replanning before the 25th fraction during the course of intensity-modulated radiotherapy for patients with nasopharyngeal carcinoma. , 2010, International journal of radiation oncology, biology, physics.

[28]  P. Lambin,et al.  Evaluation of nonrigid registration models for interfraction dose accumulation in radiotherapy. , 2009, Medical physics.

[29]  Laurent Jacques,et al.  Diffeomorphic Registration of Images with Variable Contrast Enhancement , 2010, Int. J. Biomed. Imaging.

[30]  Johannes A Langendijk,et al.  Potential benefits of scanned intensity-modulated proton therapy versus advanced photon therapy with regard to sparing of the salivary glands in oropharyngeal cancer. , 2011, International journal of radiation oncology, biology, physics.

[31]  M. V. van Herk,et al.  Adaptive radiotherapy with an average anatomy model: evaluation and quantification of residual deformations in head and neck cancer patients. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[32]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[33]  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.

[34]  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.