Monte Carlo as a four-dimensional radiotherapy treatment-planning tool to account for respiratory motion.

Four-dimensional (4D) radiotherapy is the explicit inclusion of the temporal changes in anatomy during the imaging, planning and delivery of radiotherapy. Temporal anatomic changes can occur for many reasons, though the focus of the current investigation was respiration motion for lung tumours. The aims of the current research were first to develop a 4D Monte Carlo methodology and second to apply this technique to an existing 4D treatment plan. A 4D CT scan consisting of a series of 3D CT image sets acquired at different respiratory phases was used. Deformable image registration was performed to map each CT set from the end-inhale respiration phase to the CT image sets corresponding with subsequent respiration phases. This deformable registration allowed the contours drawn on the end-inhale CT to be automatically drawn on the other respiratory phase CT image sets. A treatment plan was created on the end-inhale CT image set and then automatically created on each of the 3D CT image sets corresponding with subsequent respiration phases, based on the beam arrangement and dose prescription in the end-inhale plan. Dose calculation using Monte Carlo was simultaneously performed on each of the N (=8) 3D image sets with 1/N fewer particles per calculation than for a 3D plan. The dose distribution from each respiratory phase CT image set was mapped back to the end-inhale CT image set for analysis. This use of deformable image registration to merge all the statistically noisy dose distributions back onto one CT image set effectively yielded a 4D Monte Carlo calculation with a statistical uncertainty equivalent to a 3D calculation, with a similar calculation time for the 3D and 4D methods. Monte Carlo as a dose calculation tool for 4D radiotherapy planning has two advantages: (1) higher accuracy for calculation in electronic disequilibrium conditions, such as those encountered during lung radiotherapy, and (2) if deformable image registration is used, the calculation time for Monte Carlo is independent of the number of 3D CT image sets constituting a 4D CT, unlike other algorithms for which the calculation time scales linearly with the number of 3D CT image sets constituting a 4D CT.

[1]  P. Keall,et al.  Superposition dose calculation incorporating Monte Carlo generated electron track kernels. , 1996, Medical physics.

[2]  L. Papie,et al.  The leaf sweep algorithm for an immobile and moving target as an optimal control problem in radiotherapy delivery , 2003 .

[3]  M W Vannier,et al.  Image-based dose planning of intracavitary brachytherapy: registration of serial-imaging studies using deformable anatomic templates. , 2001, International journal of radiation oncology, biology, physics.

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

[5]  C. Ma,et al.  BEAM: a Monte Carlo code to simulate radiotherapy treatment units. , 1995, Medical physics.

[6]  H Shirato,et al.  Detection of lung tumor movement in real-time tumor-tracking radiotherapy. , 2001, International journal of radiation oncology, biology, physics.

[7]  H Shirato,et al.  Impact of respiratory movement on the computed tomographic images of small lung tumors in three-dimensional (3D) radiotherapy. , 2000, International journal of radiation oncology, biology, physics.

[8]  D A Jaffray,et al.  The effects of intra-fraction organ motion on the delivery of dynamic intensity modulation. , 1998, Physics in medicine and biology.

[9]  I. Kawrakow,et al.  Investigation of variance reduction techniques for Monte Carlo photon dose calculation using XVMC , 2000, Physics in medicine and biology.

[10]  I. Kawrakow,et al.  3D electron dose calculation using a Voxel based Monte Carlo algorithm (VMC). , 1996, Medical physics.

[11]  C. Ma,et al.  A Monte Carlo dose calculation tool for radiotherapy treatment planning. , 2002, Physics in medicine and biology.

[12]  R Mohan,et al.  Monte Carlo dose calculations for dynamic IMRT treatments. , 2001, Physics in medicine and biology.

[13]  Paul J. Keall,et al.  Performance benchmarks of the MCV Monte Carlo system , 2000 .

[14]  P. Keall 4-dimensional computed tomography imaging and treatment planning. , 2004, Seminars in radiation oncology.

[15]  J O Deasy Denoising of electron beam Monte Carlo dose distributions using digital filtering techniques. , 2000, Physics in medicine and biology.

[16]  G. Christensen,et al.  A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing. , 2003, Medical physics.

[17]  T Pawlicki,et al.  Removing the effect of statistical uncertainty on dose-volume histograms from Monte Carlo dose calculations. , 2000, Physics in medicine and biology.

[18]  H Anno,et al.  Minimum scan speeds for suppression of motion artifacts in CT. , 1992, Radiology.

[19]  Icru Prescribing, recording, and reporting photon beam therapy , 1993 .

[20]  D J Conces,et al.  Motion artifacts on CT simulate bronchiectasis. , 1988, AJR. American journal of roentgenology.

[21]  I. Kawrakow VMC++, Electron and Photon Monte Carlo Calculations Optimized for Radiation Treatment Planning , 2001 .

[22]  J. Adler,et al.  Robotic motion compensation for respiratory movement during radiosurgery. , 2000, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

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

[24]  J. F. Briesmeister MCNP-A General Monte Carlo N-Particle Transport Code , 1993 .

[25]  Joos V Lebesque,et al.  Biologic and physical fractionation effects of random geometric errors. , 2003, International journal of radiation oncology, biology, physics.

[26]  R Y Levine,et al.  Optimum beam configurations in tomographic intensity modulated radiation therapy. , 2000, Physics in medicine and biology.

[27]  Michael I. Miller,et al.  Deformable templates using large deformation kinematics , 1996, IEEE Trans. Image Process..

[28]  Katsuyuki Taguchi,et al.  Temporal resolution and the evaluation of candidate algorithms for four-dimensional CT. , 2003, Medical physics.

[29]  J Sempau,et al.  Towards the elimination of Monte Carlo statistical fluctuation from dose volume histograms for radiotherapy treatment planning. , 2000, Physics in medicine and biology.

[30]  R. Mohan,et al.  Comparison of EGS4 and MCNP4b Monte Carlo codes for generation of photon phase space distributions for a Varian 2100C. , 1999, Physics in medicine and biology.

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

[32]  M. Ding,et al.  Dose correlation for thoracic motion in radiation therapy of breast cancer. , 2003, Medical physics.

[33]  4-Dimensional radiotherapy planning , 2003 .

[34]  R. Mohan,et al.  Motion adaptive x-ray therapy: a feasibility study , 2001, Physics in medicine and biology.

[35]  R M Henkelman,et al.  The double-fissure sign: a motion artifact on thin-section CT scans. , 1987, Radiology.

[36]  R. Mohan,et al.  The effect of dose calculation uncertainty on the evaluation of radiotherapy plans. , 2000, Medical physics.

[37]  C. J. Ritchie,et al.  Predictive respiratory gating: a new method to reduce motion artifacts on CT scans. , 1994, Radiology.

[38]  C. Ling,et al.  Respiration-correlated spiral CT: a method of measuring respiratory-induced anatomic motion for radiation treatment planning. , 2002, Medical physics.

[39]  Sartaj Sahni,et al.  Leaf sequencing algorithms for segmented multileaf collimation. , 2003, Physics in medicine and biology.

[40]  Joseph O Deasy,et al.  Accelerating Monte Carlo simulations of radiation therapy dose distributions using wavelet threshold de-noising. , 2002, Medical physics.

[41]  Jan-Jakob Sonke,et al.  Focal spot motion of linear accelerators and its effect on portal image analysis. , 2003, Medical physics.

[42]  Steve B. Jiang,et al.  Effects of intra-fraction motion on IMRT dose delivery: statistical analysis and simulation. , 2002, Physics in medicine and biology.

[43]  H Paganetti,et al.  Four-dimensional Monte Carlo simulation of time-dependent geometries , 2004, Physics in medicine and biology.

[44]  Tinsu Pan,et al.  4D computed tomography for treatment planning , 2003 .

[45]  Michael I. Miller,et al.  Landmark matching via large deformation diffeomorphisms , 2000, IEEE Trans. Image Process..

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

[47]  S. K. Hilal,et al.  The tuning fork artifact in computerized tomography , 1979 .

[48]  Paul A. Yushkevich,et al.  Multiscale deformable model segmentation and statistical shape analysis using medial descriptions , 2002, IEEE Transactions on Medical Imaging.

[49]  R. Mohan,et al.  Quantifying the effect of intrafraction motion during breast IMRT planning and dose delivery. , 2003, Medical physics.

[50]  M Gambaccini,et al.  Dual-energy tissue cancellation in mammography with quasi-monochromatic x-rays. , 2002, Physics in medicine and biology.

[51]  Linda Hong,et al.  The effects of intra-fraction organ motion on the delivery of intensity-modulated field with a multileaf collimator. , 2003, Medical physics.

[52]  J Siebers,et al.  Validation of Monte Carlo generated phase-space descriptions of medical linear accelerators. , 1999, Medical physics.

[53]  Steve B. Jiang,et al.  An experimental investigation on intra-fractional organ motion effects in lung IMRT treatments. , 2003, Physics in medicine and biology.

[54]  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.

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

[56]  R. Mohan,et al.  Determining the incident electron fluence for Monte Carlo-based photon treatment planning using a standard measured data set. , 2003, Medical Physics (Lancaster).

[57]  U. Grenander,et al.  Statistical methods in computational anatomy , 1997, Statistical methods in medical research.

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

[59]  Michael I. Miller,et al.  Volumetric transformation of brain anatomy , 1997, IEEE Transactions on Medical Imaging.

[60]  R. Mohan,et al.  Converting absorbed dose to medium to absorbed dose to water for Monte Carlo based photon beam dose calculations. , 2000, Physics in medicine and biology.

[61]  H. Neuenschwander,et al.  A macro Monte Carlo method for electron beam dose calculations , 1992 .