Quantitative assessment of anatomical change using a virtual proton depth radiograph for adaptive head and neck proton therapy

The aim of this work is to demonstrate the feasibility of using water‐equivalent thickness (WET) and virtual proton depth radiographs (PDRs) of intensity corrected cone‐beam computed tomography (CBCT) to detect anatomical change and patient setup error to trigger adaptive head and neck proton therapy. The planning CT (pCT) and linear accelerator (linac) equipped CBCTs acquired weekly during treatment of a head and neck patient were used in this study. Deformable image registration (DIR) was used to register each CBCT with the pCT and map Hounsfield units (HUs) from the planning CT (pCT) onto the daily CBCT. The deformed pCT is referred as the corrected CBCT (cCBCT). Two dimensional virtual lateral PDRs were generated using a ray‐tracing technique to project the cumulative WET from a virtual source through the cCBCT and the pCT onto a virtual plane. The PDRs were used to identify anatomic regions with large variations in the proton range between the cCBCT and pCT using a threshold of 3 mm relative difference of WET and 3 mm search radius criteria. The relationship between PDR differences and dose distribution is established. Due to weight change and tumor response during treatment, large variations in WETs were observed in the relative PDRs which corresponded spatially with an increase in the number of failing points within the GTV, especially in the pharynx area. Failing points were also evident near the posterior neck due to setup variations. Differences in PDRs correlated spatially to differences in the distal dose distribution in the beam's eye view. Virtual PDRs generated from volumetric data, such as pCTs or CBCTs, are potentially a useful quantitative tool in proton therapy. PDRs and WET analysis may be used to detect anatomical change from baseline during treatment and trigger further analysis in adaptive proton therapy. PACS number(s): 87.55‐x, 87.55.‐D, 87.57.Q‐

[1]  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.

[2]  E Pedroni,et al.  Multiple Coulomb scattering and spatial resolution in proton radiography. , 1994, Medical physics.

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

[4]  L. S. Skaggs,et al.  Initial Test of a Proton Radiographic System , 1975, IEEE Transactions on Nuclear Science.

[5]  E. Pedroni,et al.  The calibration of CT Hounsfield units for radiotherapy treatment planning. , 1996, Physics in medicine and biology.

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

[7]  D A Low,et al.  A software tool for the quantitative evaluation of 3D dose calculation algorithms. , 1998, Medical physics.

[8]  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.

[9]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[10]  A. Seiden,et al.  Toward proton computed tomography , 2002, IEEE Transactions on Nuclear Science.

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

[12]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[13]  Jay B. West,et al.  The distribution of target registration error in rigid-body point-based registration , 2001, IEEE Transactions on Medical Imaging.

[14]  Barbara Kaser-Hotz,et al.  Patient specific optimization of the relation between CT-hounsfield units and proton stopping power with proton radiography. , 2004, Medical physics.

[15]  S M Holloway,et al.  A method for acquiring random range uncertainty probability distributions in proton therapy , 2017, Physics in medicine and biology.

[16]  P Zygmanski,et al.  The measurement of proton stopping power using proton-cone-beam computed tomography. , 2000, Physics in medicine and biology.

[17]  S. Peggs,et al.  Conceptual design of a proton computed tomography system for applications in proton radiation therapy , 2004, IEEE Transactions on Nuclear Science.

[18]  Lei Xing,et al.  Multiscale registration of planning CT and daily cone beam CT images for adaptive radiation therapy. , 2009, Medical physics.

[19]  Hsiao-Ming Lu,et al.  A potential method for in vivo range verification in proton therapy treatment , 2008, Physics in medicine and biology.

[20]  E Pedroni,et al.  Proton radiography as a tool for quality control in proton therapy. , 1995, Medical physics.

[21]  Barbara Kaser-Hotz,et al.  First proton radiography of an animal patient. , 2004, Medical physics.

[22]  George Starkschall,et al.  Effects of interfractional motion and anatomic changes on proton therapy dose distribution in lung cancer. , 2008, International journal of radiation oncology, biology, physics.

[23]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[24]  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.

[25]  A J Lomax,et al.  Proton range verification using a range probe: definition of concept and initial analysis , 2010, Physics in medicine and biology.

[26]  Wayne D Newhauser,et al.  Calculation of water equivalent thickness of materials of arbitrary density , elemental composition and thickness in proton beam irradiation , 2009 .

[27]  J. Cygler,et al.  Commissioning and quality assurance of treatment planning computers. , 1993, International journal of radiation oncology, biology, physics.

[28]  Katia Parodi,et al.  Investigating CT to CBCT image registration for head and neck proton therapy as a tool for daily dose recalculation. , 2015, Medical physics.

[29]  Jun Li,et al.  Feasibility of improving cone‐beam CT number consistency using a scatter correction algorithm , 2013, Journal of applied clinical medical physics.

[30]  Zuofeng Li,et al.  A Technical Guide for Passive Scattering Proton Radiation Therapy for Breast Cancer. , 2017, International journal of particle therapy.