Quantifying variability in radiation dose due to respiratory-induced tumor motion

Medical Image Analysis

Volumn 15, Issue 4, 2011, Pages 640-649

  Geneser, S E ^a   Hinkle, J D ^a   Kirby, R M ^a   Wang, B ^b   Salter, B ^b   Joshi, S ^a  

^a UNIVERSITY OF UTAH   (United States)

^b UNIVERSITY OF UTAH   (United States)

Author keywords

Polynomial chaos; Respiratory induced dose variation; Stereotactic body radiation therapy; Stochastic collocation; Stochastic modeling

Indexed keywords

DOSE VARIATIONS; POLYNOMIAL CHAOS; STEREOTACTIC BODY RADIATION THERAPY; STOCHASTIC COLLOCATION; STOCHASTIC MODELING;

COMPUTERIZED TOMOGRAPHY; DEFORMATION; DOSIMETRY; FORECASTING; PHYSIOLOGY; PROBABILITY DENSITY FUNCTION; RADIOTHERAPY; RANDOM PROCESSES; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; TOMOGRAPHY; TUMORS;

RADIATION;

AMPLITUDE MODULATION; ARTICLE; BREATHING; BREATHING PATTERN; COMPUTER ASSISTED TOMOGRAPHY; CONCEPTUAL FRAMEWORK; DOSIMETRY; IMAGE ANALYSIS; MATHEMATICAL ANALYSIS; MONTE CARLO METHOD; MOTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROBABILITY DENSITY FUNCTION; RADIATION DOSE; RADIATION DOSE DISTRIBUTION; RESPIRATORY INDUCED ORGAN MOTION; SIGNAL NOISE RATIO; SPIRAL COMPUTER ASSISTED TOMOGRAPHY; STEREOTACTIC BODY RADIATION THERAPY; STOCHASTIC MODEL; THORAX WALL; TREATMENT PLANNING;

BODY BURDEN; HUMANS; LUNG NEOPLASMS; MOTION; RADIATION DOSAGE; RADIATION PROTECTION; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOTHERAPY, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; RESPIRATORY MECHANICS; RESPIRATORY-GATED IMAGING TECHNIQUES; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 79958773259     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2010.07.003     Document Type: Article

References (68)

1. Abdelnour A., Nehmeh S., Pan T., Humm J., Vernon P., Schröder H., Rosenzweig K., Mageras G., Yorke E., Larson S., Erdi Y. Phase and amplitude binning for 4D-CT imaging. Phys. Med. Biol. 2007, 52(May):3515-3529.
2. Babuška, I., Nobile, F., Tempone, R., 2005. A stochastic collocation method for elliptic partial differential equations with random input data. Tech. Rep. ICES 05-47, The University of Texas at Austin.
3. Beddar A., Kainz K., Briere T., Tsunashima Y., Pan T., Prado K., Mohan R., Gillin M., Krishnan S. Correlation between internal fiducial tumor motion and external marker motion for liver tumors imaged with 4D-CT. Int. J. Radiat. Oncol. Biol. Phys. 2007, 67(2):630-638.
4. Beg M.F., Miller M.I., Trouvé A., Younes L. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vision 2005, 61(2):139-157.
5. Boldea V., Sharp G., Jiang S., Sarrut D. 4D-CT lung motion estimation with deformable registration: quantification of motion nonlinearity and hysteresis. Med. Phys. 2008, 35(3):1008-1018.
6. Bortfeld T., Jokivarsi K., Goitein M., Kung J., Jiang S. Effects of intra-fraction motion on IMRT dose delivery: statistical analysis and simulation. Phys. Med. Biol. 2002, 47:2203-2220.
7. Bortfeld T., Jiang S., Rietzel E. Effects of motion on the total dose distribution. Semin. Radiat. Oncol. 2004, 14:41-51.
8. Brandner E., Wu A., Chen H., Heron D., Kalnicki S., Komanduri K., Gerszten K., Burton S., Ahmed I., Shou Z. Abdominal organ motion measured using 4D CT. Int. J. Radiat. Oncol. Biol. Phys. 2006, 65:554-560.
9. Caflisch R. Monte Carlo and quasi-Monte Carlo methods. Acta Numerica 1998, vol. 7. Cambridge University Press.
10. Chi P., Balter P., Luo D., Mohan R., Pan T. Relation of external surface to internal tumor motion studied with CINE CT. Med. Phys. 2006, 33(9):3116-3123.
11. Christensen G.E., Rabbitt R.D., Miller M.I. Deformable templates using large deformation kinematics. IEEE Trans. Image Process. 1996, 5(10):1435-1447.
12. Chui C., Yorke E., Hong L. The effects of intra-fraction organ motion on the delivery of intensity-modulated field with a multileaf collimator. Med. Phys. 2003, 30:1736-1746.
13. Dempster A., Laird N., Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B: Met. 1977, 39(1):1-38.
14. Dupuis P., Grenander U., Miller M.I. Variational problems on flows of diffeomorphisms for image matching. Quart. J. Appl. Math. 1998, 56(3):587-600.
15. Ford E., Mageras G., Yorke E., Ling C. Respiration-correlated spiral CT: a method of measuring respiratory-induced anatomic motion for radiation treatment planning. Med. Phys. 2003, 30:88-97.
16. Foskey M., Davis B., Goyal L., Chang S., Chaney E., Strehl N., Tomei S., Rosenman J., Joshi S. Large deformation three-dimensional image registration in image-guided radiation therapy. Phys. Med. Biol. 2005, 50:5869-5892.
17. Gamerman L. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference 1997, Chapman & Hall, London, England.
18. Ganapathysubramanian B., Zabaras N. Sparse grid collocation schemes for stochastic natural convection problems. J. Comput. Phys. 2007, 225(1):652-685.
19. Geneser S., Kirby R., Wang B., Salter B., Joshi S. Incorporating patient breathing variations into a stochastic model of dose deposition for stereotactic body radiation therapy. Proceedings of the 21st International Conference on Information Processing in Medical Imaging 2009, vol. 5636:688-700. Lecture Notes in Computer Science (LNCS), Williamsburg, Virginia.
20. Golub G., Welsh J. Calculation of Gauss quadrature rules. Math. Comput. 1969, 23:A1-A10.
21. Hanley J., Debois M., Raben A., et al. Deep inspiration breath-hold technique for lung tumors: the potential value of target immobilization and reduced lung density in dose escalation. Int. J. Radiat. Oncol. Biol. Phys. 1996, 36(1):188.
22. Heath E., Seuntjens J., Sheikh-Bagheri D. Dosimetric evaluation of the clinical implementation of the first commercial IMRT Monte Carlo treatment planning system at 6 MV. Med. Phys. 2004, 31:2771-2779.
23. Helton J., Davis F., Johnson J. A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling. Reliab. Eng. Syst. Safe. 2005, 89:305-330.
24. Herk M.v. Errors and margins in radiotherapy. Semin. Radiat. Oncol. 2004, 14(1):52-64.
25. Hinkle, J., Fletcher, P.T., Wang, B., Salter, B., Joshi, S., 2009. 4D MAP image reconstruction incorporating organ motion. In: IPMI 2009: Proceedings of Information Processing in Medical Imaging, pp. 676-687.
26. Ionascu D., Jiang S., Nishloka S., Shirato H., Berbeco R. Internal-external correlation investigations of respiratory induced motion of lung tumors. Med. Phys. 2007, 34(10):3893-3903.
27. Jiang S., Pope C., Al Jarrah K., Kung J., Bortfeld T., Chen G. An experimental investigation on intra-fractional organ motion effects in lung imrt treatments. Phys. Med. Biol. 2003, 48:1773-1784.
28. Jiang S., Wolfgang J., Mageras G. Quality assurance challenges for motion-adaptive radiation therapy: gating, breath holding, and four-dimensional computed tomography. Int. J. Radiat. Oncol. Biol. Phys. 2008, 71(1):S103-S107.
29. Joshi S.C., Miller M.I. Landmark matching via large deformation diffeomorphisms. IEEE Trans. Image Process. 2000, 9(8):1357-1370.
30. Keall P., Todor A., Vedam S., Bartee C., Sibers J., Kini V., Mohan R. On the use of EPID-based implanted marker tracking for 4D radiotherapy. Med. Phys. 2004, 31:3492-3499.
31. Keall P., Joshi S., Vedam S., Siebers J., Kini V., Mohan R. Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking. Med. Phys. 2005, 32:942-951.
32. Keall P., Kini V., Vedam S., Mohan R. Potential radiotherapy improvements with respiratory gating. Australas. Phys. Eng. Sci. Med. 2005, 25:1-6.
33. Keall P., Mageras G., Balter J., Emery R., Forster K., Jiang S., Kapatoes J., Low D., Murphy M., Murray B., Ramsey C., Van Herk M., Vedam S., Wong J., Yorke R. The management of respiratory motion in radiation oncology report of AAPM Task Group (76a). Med. Phys. 2006, 33:3874-3900.
34. Kubo H., Len P., Minohara S., Mostafavi H. Breathing-synchronized radiotherapy program at the University of California Davis Cancer Center. Med. Phys. 2000, 27(2):346-353.
35. Kuo H.-H. Gaussian Measures in Banach Spaces. Lecture Notes in Mathematics 1975, vol. 463. Springer-Verlag, Berlin, Heidelberg, New York.
36. Loh W. On Latin hypercube sampling. Ann. Stat. 1996, 24:2058-2080.
37. Lujan A., Larsen E., Balter J., Ten Haken R. A method for incorporating organ motion due to breathing into 3D dose calculations. Med. Phys. 1999, 26(5):715-720.
38. Lujan A., Larsen E., Balter J., Ten Haken R. A method for incorporating organ motion due to breathing into 3D dose calculations: sensitivity to variations in motion. Med. Phys. 2003, 10:2643-2649.
39. Mathelin, L., Hussaini, M., 2003. A stochastic collocation algorithm for uncertainty analysis. Tech. Rep. NASA/CR-2003-212153, NASA Langley Research Center.
40. McGarry R., Papiez L., Williams M., Whitofrd T., Timmerman R. Stereotactic body radiation therapy of early-stage non-small-cell lung carcinoma: phase I study. Int. J. Radiat. Oncol. Biol. Phys. 2005, 64:1010-1015.
41. McLachlan G., Peel D. Finite Mixture Models 2000, John Wiley & Sons, Inc., New York, NY.
42. Morokoff W., Caflisch R. Quasi-Monte Carlo integration. J. Cell Physiol. 1995, 122(2):218-230.
43. Naqvi S.A., D'Souza W.D., Yu C. Real-time intra-fraction-motion tracking using the treatment couch: a feasibility study. Phys. Med. Biol. 2005, 50:4021-4033.
44. Nehmeh S., Erdi Y., Pan T., Yorke E., Mageras G., Rosenzweig K., Schoder H., Mostafavi H., Squire O., Pevsner A., et al. Quantitation of respiratory motion during 4D-PET/CT acquisition. Med. Phys. 2004, 31:1333-1338.
45. Neicu T., Berbeco R., Wolfgang J., Jiang S. Synchonized moving aperture radiation therapy (SMART): improvement of breathing pattern reproducibility using respiratory coaching. Phys. Med. Biol. 2006, 51:617-636.
46. Niederreiter H., Hellakalek P., Larcher G., Zinterhof P. Monte Carlo and Quasi-Monte Carlo Methods 1998, Springer-Verlag, New York, NY.
47. Nobile F., Tempone R., Webster C. A sparse grid stochastic collocation method for partial differential equations with random input data. SIAM J. Numer. Anal. 2008, 46(5):2309-2345.
48. Novak, E., Ritter, K., 1997. The curse of dimension and a universal method for numerical integration. In: Nurnberger, G., Schmidt, J., Walz, G. (Eds.), Multivariate Approximation and Splines, pp. 177-187.
49. Pearson K. On lines and planes of closest fit to systems of points in space. Philos. Mag. 1901, 2(6):559-572.
50. Pevsner A., Davis B., Joshi S., et al. Evaluation of an automated deformable image matching method for quantifying lung motion in respiration-correlated CT images. Med. Phys. 2006, 33(2):369-376.
51. Press W., Teukolsky S., Vetterling W., Flannery B. Numerical Recipes in C: The Art of Scientific Computing 1992, Cambridge University Press, New York, NY, (Chapter: Gaussian Quadratures and Orthogonal Polynomials, pp. 147-161). second ed.
52. Purdy J. Intensity-modulated radiotherapy: current status and issues of interest. Int. J. Radiat. Oncol. Biol. Phys. 2001, 51(4):880-914.
53. Quian S., Stow C., Borsuk M. On Monte Carlo methods for Bayesian inference. Ecol. Modell. 2003, 159:269-277.
54. Rassiah-Szegedi P., Salter B., Fuller C., Blough M., Papanikolaou N., Fuss M. Monte Carlo characterization of target doses in stereotactic body radiation therapy (SBRT). Acta Oncol. 2006, 45:989-994.
55. Sawant A., Venkat R., Srivastava V., Carlson D. Management of three-dimensional intrafraction motion through real-time DMLC tracking. Med. Phys. 2008, 35(5):2050-2061.
56. Siantar C.H., Walling R., Daly T., Faddegon B., Albright N., Bergstrom P., et al. Description and dosimetric verification of the PEREGRINE Monte Carlo dose calculation system for photon beams incident on a water phantom. Med. Phys. 2001, 28:122-1337.
57. Smolyak S. Quadrature and interpolation formulas for tensor products of certain classes of functions. Soviet Math. Dokl. 1963, 4:240-243.
58. Stein M. Large sample properties of simulations using Latin hypercube sampling. Technometrics 1987, 29:143-151.
59. Timmerman R., Forster K., Chinsoo C. Extracranial stereotactic radiation delivery. Semin. Radiat. Oncol. 2005, 15:202-207.
60. Vedam S., Keall P., Kini V., Mostafavi H., Shukla H., Mohan R. Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. Phys. Med. Biol. 2003, 48(1):45-62.
61. Vedam S., Docef A., Fix M., Purphy M., Keall P. Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery. Med. Phys. 2005, 32:1607-1620.
62. Wiener N. The homogeneous chaos. Am. J. Math. 1938, 60(4):897-936.
63. Wijesooriya K., Weiss E., Dill V., Dong L., Mohan R., Joshi S. Quantifying the accuracy of automated structure segmentation in 4D CT images using a deformable image registration algorithm. Med. Phys. 2008, 35(4):1251-1260.
64. Wu Q., Thongphiew D., Wang Z., Chankong V., Yin F. The impact of respiratory motion and treatment technique on stereotactic body radiation therapy for liver cancer. Med. Phys. 2008, 35(4):1440-1451.
65. Xing L., Thorndyke B., Schreibmann E., Yang Y., Li T., Kim G., Luxton G., Koong A. Overview of image-guided radiation therapy. Med. Dosim. 2006, 31(2):91-112.
66. Xiu D. Efficient collocational approach for parametric uncertainty analysis. Commun. Comput. Phys. 2007, 2(2):293-309.
67. Xiu D., Hesthaven J. High-order collocation methods for differential equations with random inputs. SIAM J. Sci. Comput. 2005, 27(3):1118-1139.
68. Xiu D., Karniadakis G. The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 2002, 24:619-644.