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/content/aapm/journal/medphys/41/9/10.1118/1.4892606
1.
1. R. Loevinger, T. F. Budinger, and E. E. Watson, MIRD Primer for Absorbed Dose Calculations (Society of Nuclear Medicine, New York, NY, 1991).
2.
2. W. E. Bolch, K. F. Eckerman, G. Sgouros, and S. R. Thomas, “MIRD pamphlet No. 21: A generalized schema for radiopharmaceutical dosimetry–standardization of nomenclature,” J. Nucl. Med. 50, 477484 (2009).
http://dx.doi.org/10.2967/jnumed.108.056036
3.
3. M. G. Stabin, R. B. Sparks, and E. Crowe, “OLINDA/EXM: The second-generation personal computer software for internal dose assessment in nuclear medicine,” J. Nucl. Med. 46, 10231027 (2005).
4.
4. W. E. Bolch, L. G. Bouchet, J. S. Robertson, B. W. Wessels, J. A. Siegel, R. W. Howell, A. K. Erdi, B. Aydogan, S. Costes, and E. E. Watson, In collaboration with the MIRD Committee, Society of Nuclear Medicine: E. E. Watson (Chair), J. S. Robertson (Task Group Leader), W. E. Bolch, A. B. Brill, N. D. Charkes, D. R. Fisher, M. T. Hays, R. W. Howell, J. A. Siegel, S. R. Thomas, and B. W. Wessels, “MIRD pamphlet No. 17: The dosimetry of nonuniform activity distributions–Radionuclide S values at the voxel level,” J. Nucl. Med. 40, 11S36S (1999).
5.
5. A. Dieudonne, R. F. Hobbs, W. E. Bolch, G. Sgouros, and I. Gardin, “Fine-resolution voxel S values for constructing absorbed dose distributions at variable voxel size,” J. Nucl. Med. 51, 16001607 (2010).
http://dx.doi.org/10.2967/jnumed.110.077149
6.
6. M. Pacilio, N. Lanconelli, S. Lo Meo, M. Betti, L. Montani, L. A. Torres Aroche, and M. A. Coca Perez, “Differences among Monte Carlo codes in the calculations of voxel S values for radionuclide targeted therapy and analysis of their impact on absorbed dose evaluations,” Med. Phys. 36, 15431552 (2009).
http://dx.doi.org/10.1118/1.3103401
7.
7. N. Lanconelli, M. Pacilio, S. Lo Meo, F. Botta, A. Di Dia, A. T. Aroche, M. A. Perez, and M. Cremonesi, “A free database of radionuclide voxel S values for the dosimetry of nonuniform activity distributions,” Phys. Med. Biol. 57, 517533 (2012).
http://dx.doi.org/10.1088/0031-9155/57/2/517
8.
8. D. W. O. Rogers, “Low energy electron transport with EGS,” Nucl. Instrum. Meth. 227, 535548 (1984).
http://dx.doi.org/10.1016/0168-9002(84)90213-4
9.
9. J. S. Hendricks and J. F. Briesmeister, “Recent MCNP developments,” IEEE Trans. Nucl. Sci. 39, 10351040 (1992).
http://dx.doi.org/10.1109/23.159755
10.
10. H. Yoriyaz, M. G. Stabin, and A. dos Santos, “Monte Carlo MCNP-4B-based absorbed dose distribution estimates for patient-specific dosimetry,” J. Nucl. Med. 42, 662669 (2001).
11.
11. J. Sempau, E. Acosta, J. Baro, J. M. Fernandez-Varea, and F. Salvat, “An algorithm for Monte Carlo simulation of coupled electron-photon transport,” Nucl. Instrum. Meth. B 132, 377390 (1997).
http://dx.doi.org/10.1016/S0168-583X(97)00414-X
12.
12. M. G. Pia, “The Geant4 Toolkit: Simulation capabilities and application results,” Nucl. Phys. B 125, 6068 (2003).
13.
13. L. Ferrer, N. Chouin, A. Bitar, A. Lisbona, and M. Bardies, “Implementing dosimetry in GATE: Dose-point kernel validation with GEANT4 4.8.1,” Cancer Biother. Radiopharm. 22, 125129 (2007).
http://dx.doi.org/10.1089/cbr.2007.304
14.
14. M. G. Stabin and H. Zaidi, “Monte Carlo codes for use in therapeutic nuclear medicine,” in Therapeutic Applications of Monte Carlo Calculations in Nuclear Medicine, edited by H. Zaidi and G. Sgouros (Institute of Physics Publishing, Bristol, 2003), pp. 133157.
15.
15. I. Kawrakow, “Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version,” Med. Phys. 27, 485498 (2000).
http://dx.doi.org/10.1118/1.598917
16.
16. B. Walters and I. Kawrakow, “DOSXYZnrc users manual,” Technical Report No. PIRS-794, RevB, 2005.
17.
17. L. Strigari, E. Menghi, M. D’Andrea, and M. Benassi, “Monte Carlo dose voxel kernel calculations of beta-emitting and auger-emitting radionuclides for internal dosimetry: A comparison between EGSnrcMP and EGS4,” Med. Phys. 33, 33833389 (2006).
http://dx.doi.org/10.1118/1.2266255
18.
18. J. Grimes, A. Celler, B. Birkenfeld, S. Shcherbinin, M. H. Listewnik, H. Piwowarska-Bilska, R. Mikołajczak, and P. Zorga, “Patient-specific radiation dosimetry of 99mTc-HYNIC-Tyr3-octreotide in neuroendocrine tumors,” J. Nucl. Med. 52, 14741481 (2011).
http://dx.doi.org/10.2967/jnumed.111.088203
19.
19. S. Shcherbinin, A. Celler, T. Belhocine, R. Vanderwerf, and A. Driedger, “Accuracy of quantitative reconstructions in SPECT/CT imaging,” Phys. Med. Biol. 53, 45954604 (2008).
http://dx.doi.org/10.1088/0031-9155/53/17/009
20.
20. J. Grimes, C. Uribe, and A. Celler, “JADA: A graphical user interface for comprehensive internal dose assessment in nuclear medicine,” Med. Phys. 40, 072501 (12pp.) (2013).
http://dx.doi.org/10.1118/1.4810963
21.
21. A. Divoli, S. Chiavassa, L. Ferrer, J. Barbet, G. D. Flux, and M. Bardies, “Effect of patient morphology on dosimetric calculations for internal irradiation as assessed by comparisons of Monte Carlo versus conventional methodologies,” J. Nucl. Med. 50, 316323 (2009).
http://dx.doi.org/10.2967/jnumed.108.056705
22.
22. T. K. Johnson and S. B. Colby, “Photon contribution to tumor dose from considerations of 131I radioloabeled antibody uptake in liver, spleen, and whole body,” Med. Phys. 20, 16671674 (1993).
http://dx.doi.org/10.1118/1.596953
23.
23. D. M. Howard, K. J. Kearfott, S. J. Wilderman, and Y. K. Dewaraja, “Comparison of I-131 radioimmunotherapy tumor dosimetry: Unit density sphere model versus patient-specific Monte Carlo calculations,” Cancer Biother. Radiopharm. 26, 615621 (2011).
http://dx.doi.org/10.1089/cbr.2011.0965
24.
24. A. K. Erdi, E. D. Yorke, M. H. Loew, Y. E. Erdi, M. Sarfaraz, and B. W. Wessels, “Use of the fast Hartley transform for three-dimensional dose calculation in radionuclide therapy,” Med. Phys. 25, 22262233 (1998).
http://dx.doi.org/10.1118/1.598422
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/content/aapm/journal/medphys/41/9/10.1118/1.4892606
2014-08-14
2016-09-25

Abstract

The authors’ objective was to compare internal dose estimates obtained using the Organ Level Dose Assessment with Exponential Modeling (OLINDA/EXM) software, the voxel S value technique, and Monte Carlo simulation. Monte Carlo dose estimates were used as the reference standard to assess the impact of patient-specific anatomy on the final dose estimate.

Six patients injected with99mTc-hydrazinonicotinamide-Tyr3-octreotide were included in this study. A hybrid planar/SPECT imaging protocol was used to estimate 99mTc time-integrated activity coefficients (TIACs) for kidneys, liver, spleen, and tumors. Additionally, TIACs were predicted for 131I, 177Lu, and 90Y assuming the same biological half-lives as the 99mTc labeled tracer. The TIACs were used as input for OLINDA/EXM for organ-level dose calculation and voxel level dosimetry was performed using the voxel S value method and Monte Carlo simulation. Dose estimates for 99mTc, 131I, 177Lu, and 90Y distributions were evaluated by comparing (i) organ-level S values corresponding to each method, (ii) total tumor and organ doses, (iii) differences in right and left kidney doses, and (iv) voxelized dose distributions calculated by Monte Carlo and the voxel S value technique.

The S values for all investigated radionuclides used by OLINDA/EXM and the corresponding patient-specific S values calculated by Monte Carlo agreed within 2.3% on average for self-irradiation, and differed by as much as 105% for cross-organ irradiation. Total organ doses calculated by OLINDA/EXM and the voxel S value technique agreed with Monte Carlo results within approximately ±7%. Differences between right and left kidney doses determined by Monte Carlo were as high as 73%. Comparison of the Monte Carlo and voxel S value dose distributions showed that each method produced similar dose volume histograms with a minimum dose covering 90% of the volume (D90) agreeing within ±3%, on average.

Several aspects of OLINDA/EXM dose calculation were compared with patient-specific dose estimates obtained using Monte Carlo. Differences in patient anatomy led to large differences in cross-organ doses. However, total organ doses were still in good agreement since most of the deposited dose is due to self-irradiation. Comparison of voxelized doses calculated by Monte Carlo and the voxel S value technique showed that the 3D dose distributions produced by the respective methods are nearly identical.

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