Design and Testing of a Simulation Framework for Dosimetric Motion Studies Integrating an Anthropomorphic Computational Phantom into Four-dimensional Monte Carlo

We have designed a simulation framework for motion studies in radiation therapy by integrating the anthropomorphic NCAT phantom into a 4D Monte Carlo dose calculation engine based on DPM. Representing an artifact-free environment, the system can be used to identify class solutions as a function of geometric and dosimetric parameters. A pilot dynamic conformal study for three lesions (~ 2.0 cm) in the right lung was performed (70 Gy prescription dose). Tumor motion changed as a function of tumor location, according to the anthropomorphic deformable motion model. Conformal plans were simulated with 0 to 2 cm margin for the aperture, with additional 0.5 cm for beam penumbra. The dosimetric effects of intensity modulated radiotherapy (IMRT) vs. conformal treatments were compared in a static case. Results show that the Monte Carlo simulation framework can model tumor tracking in deformable anatomy with high accuracy, providing absolute doses for IMRT and conformal radiation therapy. A target underdosage of up to 3.67 Gy (lower lung) was highlighted in the composite dose distribution mapped at exhale. Such effects depend on tumor location and treatment margin and are affected by lung deformation and ribcage motion. In summary, the complexity in the irradiation of moving targets has been reduced to a controlled simulation environment, where several treatment options can be accurately modeled and quantified The implemented tools will be utilized for extensive motion study in lung/liver irradiation.

[1]  Shinichi Shimizu,et al.  Organ motion in image-guided radiotherapy: lessons from real-time tumor-tracking radiotherapy , 2007, International Journal of Clinical Oncology.

[2]  Iwan Kawrakow,et al.  On the condensed history technique for electron transport , 1998 .

[3]  D. Lalush,et al.  A realistic spline-based dynamic heart phantom , 1998, 1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255).

[4]  I. Kawrakow,et al.  History by history statistical estimators in the BEAM code system. , 2002, Medical physics.

[5]  Eike Rietzel,et al.  Design of 4D treatment planning target volumes. , 2006, International journal of radiation oncology, biology, physics.

[6]  George T. Y. Chen,et al.  Artifacts in computed tomography scanning of moving objects. , 2004, Seminars in radiation oncology.

[7]  Cedric X. Yu,et al.  Real-time intra-fraction-motion tracking using the treatment couch: a feasibility study , 2005, Physics in medicine and biology.

[8]  T. Bortfeld,et al.  Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions. , 2000, Physics in medicine and biology.

[9]  I. Gibbs,et al.  Frameless image-guided intracranial and extracranial radiosurgery using the Cyberknife robotic system. , 2006, Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique.

[10]  Harald Paganetti,et al.  Monte Carlo simulations with time-dependent geometries to investigate effects of organ motion with high temporal resolution. , 2004, International journal of radiation oncology, biology, physics.

[11]  David R. Gilland,et al.  Estimation of images and nonrigid deformations in gated emission CT , 2006, IEEE Transactions on Medical Imaging.

[12]  I. Kawrakow Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version. , 2000, Medical physics.

[13]  H. Hansen,et al.  Lung cancer. , 1990, Cancer chemotherapy and biological response modifiers.

[14]  J. Seco,et al.  Head-and-neck IMRT treatments assessed with a Monte Carlo dose calculation engine , 2005, Physics in medicine and biology.

[15]  Steve B. Jiang Radiotherapy of mobile tumors. , 2006, Seminars in radiation oncology.

[16]  Joao Seco,et al.  Conversion of CT numbers into tissue parameters for Monte Carlo dose calculations: a multi-centre study , 2007, Physics in medicine and biology.

[17]  V. Spitzer,et al.  The visible human dataset: The anatomical platform for human simulation , 1998, The Anatomical record.

[18]  Eike Rietzel,et al.  Four-dimensional proton treatment planning for lung tumors. , 2006, International journal of radiation oncology, biology, physics.

[19]  Indrin J Chetty,et al.  The impact of breathing motion versus heterogeneity effects in lung cancer treatment planning. , 2007, Medical physics.

[20]  Harald Paganetti,et al.  SU-GG-T-317: Impact of Tumor Motion and Size in the Irradiation of Moving Tumors in Step-And-Shoot IMRT: A NCAT Based 4D Monte Carlo Simulation Study , 2008 .

[21]  H Paganetti,et al.  Effects of organ motion on IMRT treatments with segments of few monitor units. , 2007, Medical physics.

[22]  S. Sastryvedam GEOMETRIC ACCURACY OF A REAL-TIME TARGET TRACKING SYSTEM WITH DYNAMIC MULTILEAF COLLIMATOR TRACKING SYSTEM , 2006 .

[23]  Habib Zaidi,et al.  Computational anthropomorphic models of the human anatomy: the path to realistic Monte Carlo modeling in radiological sciences. , 2007, Annual review of biomedical engineering.

[24]  Paul Keall,et al.  The clinical implementation of respiratory-gated intensity-modulated radiotherapy. , 2006, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[25]  Y D Mutaf,et al.  The impact of temporal inaccuracies on 4DCT image quality. , 2007, Medical physics.

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

[27]  George Starkschall,et al.  Assessing respiration-induced tumor motion and internal target volume using four-dimensional computed tomography for radiotherapy of lung cancer. , 2007, International journal of radiation oncology, biology, physics.

[28]  Joe Y. Chang,et al.  4D Proton treatment planning strategy for mobile lung tumors. , 2007, International journal of radiation oncology, biology, physics.

[29]  Martin Caon,et al.  Voxel-based computational models of real human anatomy: a review , 2004, Radiation and Environmental Biophysics.

[30]  A. Trofimov,et al.  4D Monte Carlo simulation of proton beam scanning: modelling of variations in time and space to study the interplay between scanning pattern and time-dependent patient geometry , 2005, Physics in medicine and biology.

[31]  B.M.W. Tsui,et al.  Development of a dynamic model for the lung lobes and airway tree in the NCAT phantom , 2002 .

[32]  R Mohan,et al.  Monte Carlo as a four-dimensional radiotherapy treatment-planning tool to account for respiratory motion. , 2004, Physics in medicine and biology.

[33]  R. Mohan,et al.  A method for photon beam Monte Carlo multileaf collimator particle transport. , 2002, Physics in medicine and biology.

[34]  Joao Seco,et al.  Dosimetric impact of motion in free-breathing and gated lung radiotherapy: a 4D Monte Carlo study of intrafraction and interfraction effects. , 2007, Medical physics.

[35]  J. Sempau,et al.  DPM, a fast, accurate Monte Carlo code optimized for photon and electron radiotherapy treatment planning dose calculations , 2000 .