A direct voxel tracking method for four-dimensional Monte Carlo dose calculations in deforming anatomy.

In this work we present a method of calculating dose in deforming anatomy where the position and shape of each dose voxel is tracked as the anatomy changes. The EGSnrc/DOSXYZnrc Monte Carlo code was modified to calculate dose in voxels that are deformed according to deformation vectors obtained from a nonlinear image registration algorithm. The defDOSXYZ code was validated by consistency checks and by comparing calculations against DOSXYZnrc calculations. Calculations in deforming phantoms were compared with a dose remapping method employing trilinear interpolation. Dose calculations with the deforming voxels agree with DOSXYZnrc calculations within 1%. In simple deforming rectangular phantoms the trilinear dose remapping method was found to underestimate the dose by up to 29% for a 1.0 cm voxel size within the field, with larger discrepancies in regions of steep dose gradients. The agreement between the two calculation methods improved with smaller voxel size and deformation magnitude. A comparison of dose remapping from Inhale to Exhale in an anatomical breathing phantom demonstrated that dose deformations are underestimated by up to 16% in the penumbra and 8% near the surface with trilinear interpolation.

[1]  Geoffrey G. Zhang,et al.  Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method. , 2004, Physics in medicine and biology.

[2]  T Hartkens.  Measuring, analysing and visualising brain deformation using non-rigid registration. , 2003 .

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

[4]  U. Fano,et al.  Note on the Bragg-Gray cavity principle for measuring energy dissipation. , 1954, Radiation research.

[5]  J M Balter,et al.  Technical note: creating a four-dimensional model of the liver using finite element analysis. , 2002, Medical physics.

[6]  Randall K Ten Haken,et al.  A method for incorporating organ motion due to breathing into 3D dose calculations in the liver: sensitivity to variations in motion. , 2003, Medical physics.

[7]  G S Bauman,et al.  Tracking the dose distribution in radiation therapy by accounting for variable anatomy , 2004, Physics in medicine and biology.

[8]  D. Gladstone,et al.  A numerical simulation of organ motion and daily setup uncertainties: implications for radiation therapy. , 1997, International journal of radiation oncology, biology, physics.

[9]  D. Manocha,et al.  Development and application of the new dynamic Nurbs-based Cardiac-Torso (NCAT) phantom. , 2001 .

[10]  R Mohan,et al.  A method of incorporating organ motion uncertainties into three-dimensional conformal treatment plans. , 1996, International journal of radiation oncology, biology, physics.

[11]  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.

[12]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  D. Louis Collins,et al.  Tuning and Comparing Spatial Normalization Methods , 2003, MICCAI.

[14]  Benjamin M. W. Tsui,et al.  Modeling respiratory mechanics in the MCAT and spline-based MCAT phantoms , 1999 .

[15]  M. V. van Herk,et al.  A model to simulate day-to-day variations in rectum shape. , 2002, International journal of radiation oncology, biology, physics.

[16]  D. Louis Collins,et al.  Animal: Validation and Applications of Nonlinear Registration-Based Segmentation , 1997, Int. J. Pattern Recognit. Artif. Intell..

[17]  Indrin J Chetty,et al.  Dose reconstruction in deforming lung anatomy: dose grid size effects and clinical implications. , 2005, Medical physics.

[18]  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.

[19]  Jake Van Dyk,et al.  Limitations of a convolution method for modeling geometric uncertainties in radiation therapy. II. The effect of a finite number of fractions. , 2003, Medical physics.

[20]  J. Battista,et al.  Limitations of a convolution method for modeling geometric uncertainties in radiation therapy. I. The effect of shift invariance. , 2003, Medical physics.

[21]  D L McShan,et al.  Inclusion of organ deformation in dose calculations. , 2003, Medical physics.

[22]  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.

[23]  D. Louis Collins,et al.  Automatic 3‐D model‐based neuroanatomical segmentation , 1995 .

[24]  Randall K Ten Haken,et al.  Alterations in normal liver doses due to organ motion. , 2003, International journal of radiation oncology, biology, physics.

[25]  D. Hill,et al.  Non-rigid image registration: theory and practice. , 2004, The British journal of radiology.

[26]  D. Yan,et al.  A model to accumulate fractionated dose in a deforming organ. , 1999, International journal of radiation oncology, biology, physics.

[27]  E. Larsen,et al.  A method for incorporating organ motion due to breathing into 3D dose calculations. , 1999, Medical physics.