Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
H. Ahmadzadehfar, H. Biersack, and S. Ezziddin, “Radioembolization of liver tumors with yttrium-90 microspheres,” Semin. Nucl. Med. 40, 105121 (2010).
J. Ingold, G. Reed, and H. Kaplan, “Radiation hepatitis,” Am. J. Roentgenol. 93, 200208 (1965).
R. Salem and K. G. Thurston, “Radioembolization with 90Y ttrium microspheres: A state-of-the-art brachytherapy treatment for primary and secondary liver malignancies: Part 1—Technical and methodologic considerations,” J. Vasc. Interv. Radiol. 17, 12511278 (2006).
S. A. Gulec and J. A. Siegel, “Posttherapy radiation safety considerations in radiomicrosphere treatment with 90Y-microspheres,” J. Nucl. Med. 48, 20802086 (2007).
Sirtex: SIR-Spheres® microspheres, Training Program Physicians and Institutions, available at nz_and_asia.pdf, accessed June 05, 2015.
TheraSphereTM Yttrium-90 Glass Microspheres Instruction for Use, available at, accessed June 05, 2015.
F. Giammarile, L. Bodei, C. Chiesa, G. Flux, F. Forrer, F. Kraeber-Bodere, B. Brans, B. Lambert, M. Konijnenberg, F. Borson-Chazot, J. Tennvall, and M. Luster, “EANM procedure guideline for the treatment of liver cancer and liver metastases with intra-arterial radioactive compounds,” Eur. J. Nucl. Med. Mol. Imaging 38, 13931406 (2011).
S. A. Gulec, G. Mesoloras, and M. Stabin, “Dosimetric techniques in Y-90 microsphere therapy of liver cancer: The MIRD equation for dose calculations,” J. Nucl. Med. 47, 12091211 (2006).
W. A. Dezarn, J. T. Cessna, L. A. DeWerd, W. Feng, V. L. Gates, J. Halama, A. S. Kennedy, S. Nag, M. Sarfaraz, V. Sehgal, R. Selwyn, M. G. Stabin, B. R. Thomadsen, L. E. Williams, and R. Salem, “Recommendations of the American Association of Physicists in Medicine on dosimetry, imaging, and quality assurance procedures for 90Y microsphere brachytherapy in the treatment of hepatic malignancies,” Med. Phys. 38, 48244845 (2011).
S. Ho et al., “Partition model for estimating radiation doses from yttrium-90 microspheres in treating hepatic tumours,” Eur. J. Nucl. Med. 23, 947952 (1996).
Y. H. Kao, E. H. Tan, T. K. Teo, C. E. Ng, and S. W. Goh, “Imaging discordance between hepatic angiography versus Tc-99m-MAA SPECT/CT: A case series, technical discussion and clinical implications,” Ann. Nucl. Med. 25, 669676 (2011).
M. Maccauro et al., “Multiagent imaging of liver tumors with reference to intra-arterial radioembolization,” Clin. Transl. Imaging 1, 423432 (2013).
C. Van de Wiele et al., “Sirt of liver metastases: Physiological and pathophysiological considerations,” Eur. J. Nucl. Med. Mol. Imaging 39, 16461655 (2012).
H. Amthauer, G. Ulrich, O. S. Grosser, and J. Ricke, “Reply: Pretreatment dosimetry in HCC radioembolization with 90Y glass microspheres cannot be invalidated with a bare visual evaluation of 99mTc-MAA uptake of colorectal metastases treated with resin microspheres,” J. Nucl. Med. 55, 12161218 (2014).
C. Chiesa et al., “Pretreatment dosimetry in HCC radioembolization with 90Y glass microspheres cannot be invalidated with a bare visual evaluation of 99mTc-MAA uptake of colorectal metastases treated with resin microspheres,” J. Nucl. Med. 55, 12151216 (2014).
C. Chiesa et al., “Radioembolization of hepatocarcinoma with 90Y glass Microspheres: Development of an individualized treatment planning strategy based on dosimetry and radiobiology,” Eur. J. Nucl. Med. Mol. Imaging 42, 17181738 (2015).
M. Sarfaraz, A. S. Kennedy, M. A. Lodge, X. A. Li, X. Wu, and C. X. Yu, “Radiation absorbed dose distribution in a patient treated with yttrium-90 microspheres for hepatocellular carcinoma,” Med. Phys. 31, 24492453 (2004).
C. Chiesa et al., “A dosimetric treatment planning strategy in radioembolization of hepatocarcinoma with 90Y glass microspheres,” Q. J. Nucl. Med. Mol. Imaging 56, 503508 (2012).
A. Dieudonné, E. Garin, S. Laffont, Y. Rolland, R. Lebtahi, D. Leguludec, and I. Gardin, “Clinical feasibility of fast 3D dosimetry of the liver for treatment planning of hepatocellular carcinoma with 90Y-microspheres,” J. Nucl. Med. 52, 19301937 (2011).
Y. H. Kao et al., “Post-radioembolization yttrium-90 PET/CT—Part 2: Dose–response and tumor predictive dosimetry for resin microspheres,” EJNMMI Res. 3, 57 (2013).
E. Fourkal, I. Veltchev, M. Lin, S. Koren, J. Meyer, M. Doss, and J. Q. Yu, “3D inpatient dose reconstruction from the PET-CT imaging of 90Y microspheres for metastatic cancer to the liver: Feasibility study,” Med. Phys. 40, 081702 (10pp.) (2013).
M. Cremonesi, C. Chiesa, L. Strigari, M. Ferrari, F. Botta, F. Guerriero, C. De Cicco, G. Bonomo, F. Orsi, L. Bodei, A. Di Dia, C. M. Grana, and R. Orecchia, “Radioembolization of hepatic lesions from a radiobiology and dosimetric perspective,” Front. Oncol. 4, 210 (2014).
A. Petitguillaume, M. Bernardini, L. Hadid, C. de Labriolle-Vaylet, D. Franck, and A. Desbrée, “Three-dimensional personalized Monte Carlo dosimetry in 90Y resin microspheres therapy of hepatic metastases: Nontumoral liver and lungs radiation protection considerations and treatment planning optimization,” J. Nucl. Med. 55, 405413 (2014).
M. J. Guy, G. D. Flux, P. Papavasileiou, M. A. Flower, and R. J. Ott, “RMDP: A dedicated package for 131I SPECT quantification, registration and patient-specific dosimetry,” Cancer Biother. Radiopharm. 18, 6169 (2003).
Y. K. Dewaraja, S. J. Wilderman, M. Ljungberg, K. F. Koral, K. Zasadny, and M. S. Kaminiski, “Accurate dosimetry in 131I radionuclide therapy using patient-specific, 3-dimensional methods for SPECT reconstruction and absorbed dose calculation,” J. Nucl. Med. 46, 840849 (2005).
J. Lehmann, C. H. Siantar, D. E. Wessol, C. A. Wemple, D. Nigg, J. Cogliati, T. Daly, M. A. Descalle, T. Flickinger, D. Pletcher, and G. DeNardo, “Monte Carlo treatment planning for molecular targeted radiotherapy within the MINERVA system,” Phys. Med. Biol. 50, 947958 (2005).
S. Chiavassa, I. Aubineau-Lanice, A. Bitar, A. Lisbona, J. Barbet, D. Franck, J. R. Jourdain, and M. Bardies, “Validation of a personalized dosimetric evaluation tool (Oedipe) for targeted radiotherapy based on the Monte Carlo MCNPX code,” Phys. Med. Biol. 51, 601616 (2006).
A. Prideaux, H. Song, R. Hobbs, B. He, E. Frey, P. Ladenson, R. Wahl, and G. Sgouros, “3D radiobiologic dosimetry: Application of radiobiologic modelling to patient-specific 3D imaging-based internal dosimetry,” J. Nucl. Med. 48, 10081016 (2007).
F. Botta et al., “Use of the FLUKA Monte Carlo code for 3D patient-specific dosimetry on PET-CT and SPECT-CT images,” Phys. Med. Biol. 58, 80998120 (2013).
S. Marcatili, C. Pettinato, S. Daniels, G. Lewis, P. Edwards, S. Fanti, and E. Spezi, “Development and validation of RAYDOSE: A Geant4-based application for molecular radiotherapy,” Phys. Med. Biol. 58, 24912508 (2013).
W. E. Bolch et al., “Mird pamphlet No. 17: The dosimetry of nonuniform activity distributions—Radionuclide S values at the voxel level,” J. Nucl. Med. 40, 11S36S (1999).
M. Pacilio, N. Lanconelli, S. Lo Meo, M. Betti, L. Montani, A. L. Torres, and M. 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).
N. Lanconelli, M. Pacilio, S. Lo Meo, F. Botta, A. Di Dia, A. L. Torres Aroche, M. A. Coca 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).
A. Dieudonné, 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).
K. N. Tapp, W. B. Lea, M. S. Johnson, M. Tann, J. W. Fletcher, and G. D. Hutchins, “The impact of image reconstruction bias on PET/CT 90Y dosimetry after radioembolization,” J. Nucl. Med. 55, 14521458 (2014).
M. Pacilio, E. Amato, N. Lanconelli, C. Basile, L. A. Torres, F. Botta, M. Ferrari, N. Cornejo Diaz, M. Coca Perez, M. Fernández, M. Lassmann, A. Vergara Gil, and M. Cremonesi, “Differences in 3d dose distributions due to calculation method of voxel s-values and the influence of image blurring in spect,” Phys. Med. Biol. 60, 19451964 (2015).
P. Flamen, B. Vanderlinden, P. Delatte, G. Ghanem, L. Ameye, M. Van Den Eynde, and A. Hendlisz, “Multimodality imaging can predict the metabolic response of unresectable colorectal liver metastases to radioembolization therapy with Yttrium-90 labeled resin microspheres,” Phys. Med. Biol. 53, 65916603 (2008).
M. Ljungberg and K. Sjogreen-Gleisner, “The accuracy of absorbed dose estimates in tumours determined by quantitative SPECT: A Monte Carlo study,” Acta Oncol. 50, 981989 (2011).
A. S. Pasciak, A. C. Bourgeois, and Y. C. Bradley, “A comparison of techniques for 90Y PET/CT image based dosimetry following radioembolization with resin microspheres,” Front. Oncol. 4, 121 (2014).
Y. K. Dewaraja, E. C. Frey, G. Sgouros, A. B. Brill, P. Roberson, P. B. Zanzonico, and M. Ljungberg, “Mird pamphlet No. 23: Quantitative SPECT for patient-specific 3-Dimensional dosimetry in internal radionuclide therapy,” J. Nucl. Med. 53, 13101325 (2012).
K. Erlandsson, I. Buvat, P. H. Pretorius, B. A. Thomas, and B. F. Hutton, “A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology,” Phys. Med. Biol. 57, R119R159 (2012).
Y. K. Dewaraja, M. Ljungberg, A. J. Green, P. B. Zanzonico, and E. C. Frey, “Mird pamphlet No. 24: Guidelines for quantitative 131I SPECT in dosimetry applications,” J. Nucl. Med. 54, 21822188 (2013).
E. C. Frey, J. L. Humm, and M. Ljungberg, “Accuracy and precision of radioactivity quantification in nuclear medicine images,” Semin. Nucl. Med. 42, 208218 (2012).
See supplementary material at for some of the dosimetric comparisons performed in this study.[Supplementary Material]
P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 11161128 (2006).
ICRU, International Commission on Radiation Units and Measurements, Tissue Substitutes in Radiation Dosimetry and Measurement, ICRU Report 44 (International Commission on Radiation Units and Measurements, Bethesda, MD, 1989).
W. Schneider, T. Bortfeld, and W. Schlegel, “Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions,” Phys. Med. Biol. 45, 459478 (2000).
L. Cheng, R. F. Hobbs, P. W. Segars, G. Sgouros, and E. C. Frey, “Improved dose–volume histogram estimates for radiopharmaceutical therapy by optimizing quantitative SPECT reconstruction parameters,” Phys. Med. Biol. 58, 36313647 (2013).
E. Garin, L. Lenoir, Y. Rolland, J. Edeline, H. Mesbah, S. Laffont, P. Porée, B. Clément, J. L. Raoul, and E. Boucher, “Dosimetry based on 99mTc-macroaggregated albumin SPECT/CT accurately predicts tumor response and survival in hepatocellular carcinoma patients treated with 90Y-loaded glass microspheres: Preliminary results,” J. Nucl. Med. 53, 255263 (2012).
T. Rohlfing, C. R. Maurer, Jr., W. G. O’Dell, and J. Zhong, “Modeling liver motion and deformation during the respiratory cycle using intensity-based free-form registration of gated MR images,” Med. Phys. 31, 427432 (2004).
M. Ljungberg, “The SIMIND Monte Carlo program,” in Monte Carlo Calculations in Nuclear Medicine, edited by M. Ljungberg, S. E. Strand, and M. A. King (CRC Press, Taylor & Francis Group, Boca Raton, FL, 2013), pp. 111127.
B. F. Hutton, I. Buvat, and F. J. Beekman, “Review and current status of SPECT scatter correction,” Phys. Med. Biol. 56, R85R112 (2011).
M. Ljungberg, M. A. King, G. J. Hademenos, and S. E. Strand, “Comparison of four scatter correction methods using Monte Carlo simulated source distributions,” J. Nucl. Med. 35, 143151 (1994).
Y. K. Dewaraja, M. Ljungberg, and J. A. Fessler, “3-D Monte Carlo-based scatter compensation in quantitative I-131 SPECT reconstruction,” IEEE Trans. Nucl. Sci. 53, 181188 (2006).
D. A. Low, W. B. Harms, S. Mutic, and J. A. Purdy, “A technique for the quantitative evaluation of dose distributions,” Med. Phys. 25, 656661 (1998).
D. A. Low and J. F. Dempsey, “Evaluation of the gamma dose distribution comparison method,” Med. Phys. 30, 24552464 (2003).
M. Wendling, L. J. Zijp, L. N. McDermott, E. J. Smit, J. J. Sonke, B. J. Mijnheer, and M. van Herk, “A fast algorithm for gamma evaluation in 3D,” Med. Phys. 34, 16471654 (2007).
K. P. Willowson, M. Tapner, QUEST Investigator Team, and D. L. Bailey, “A multicentre comparison of quantitative 90Y PET/CT for dosimetric purposes after radioembolization with resin microspheres,” Eur. J. Nucl. Med. Mol. Imaging 42, 12021222 (2015).
Y. K. Dewaraja, M. J. Schipper, J. Shen, L. B. Smith, J. Murgic, H. Savas, E. Youssef, D. Regan, S. J. Wilderman, P. L. Roberson, M. S. Kaminski, and A. M. Avram, “Tumor-absorbed dose predicts progression-free survival following 131I-tositumomab radioimmunotherapy,” J. Nucl. Med. 55, 10471053 (2014).
Y. Petibon, C. Huang, J. Ouyang, T. G. Reese, Q. Li, A. Syrkina, Y. L. Chen, and G. El Fakhri, “Relative role of motion and PSF compensation in whole-body oncologic PET-MR imaging,” Med. Phys. 41, 042503 (12pp.) (2014).
S. Webb, “Motion effects in (intensity modulated) radiation therapy: A review,” Phys. Med. Biol. 51, R403R425 (2006).
A. Carrillo, J. L. Duerk, J. S. Lewin, and D. L. Wilson, “Semiautomatic 3-D image registration as applied to interventional MRI liver cancer treatment,” IEEE Trans. Med. Imaging 19, 175185 (2000).
G. Iaccarino, M. D’Andrea, M. Cazzato, S. Ungania, G. Pizzi, G. E. Vallati, R. Sciuto, and C. L. Maini, “A method to correct breathing effects in the dosimetry of liver radioembolization with 90Y microspheres,” Eur. J. Nucl. Med. Mol. Imaging 41, S180 (2014).
M. Maccauro et al., “Prolonged overall survival after 99mTc-MAA SPECT personalized treatment planning in radioembolization of hepatocarcinoma with 90Y glass microspheres: Preliminary results of a 2 cohort study,” Eur. J. Nucl. Med. Mol. Imaging 42, S172 (2015).
M. Pacilio, C. Basile, S. Shcherbinin, F. Caselli, G. Ventroni, D. Aragno, L. Mango, and E. Santini, “An innovative iterative thresholding algorithm for tumour segmentation and volumetric quantification on SPECT images: Monte Carlo-based methodology and validation,” Med. Phys. 38, 30503061 (2011).
W. Jentzen, L. Freudenberg, E. G. Eising, M. Heinze, W. Brandau, and A. Bockisch, “Segmentation of PET volumes by iterative image thresholding,” J. Nucl. Med. 48, 108114 (2007).
H. Vees, S. Senthamizhchelvan, R. Miralbell, D. C. Weber, O. Ratib, and H. Zaidi, “Assessment of various strategies for 18F-FET PET-guided delineation of target volumes in high-grade glioma patients,” Eur. J. Nucl. Med. Mol. Imaging 36, 182193 (2009).
Activating a proper flag, the SIMIND simulation yields, as first output image, the density map down-sampled to the SPECT matrix size and co-registered with the SPECT image set. So, it is possible to perform co-registration and down-sampling of the input activity map, just pretending that it is a density map and performing a “fake” simulation.

Data & Media loading...


Article metrics loading...



Many centers aim to plan liver transarterial radioembolization (TARE) with dosimetry, even without CT-based attenuation correction (AC), or with unoptimized scatter correction (SC) methods. This work investigates the impact of presence vs absence of such corrections, and limited spatial resolution, on 3D dosimetry for TARE.

Three voxelized phantoms were derived from CT images of real patients with different body sizes. Simulations of 99mTc-SPECT projections were performed with the SIMIND code, assuming three activity distributions in the liver: uniform, inside a “liver’s segment,” or distributing multiple uptaking nodules (“nonuniform liver”), with a tumoral liver/healthy parenchyma ratio of 5:1. Projection data were reconstructed by a commercial workstation, with OSEM protocol not specifically optimized for dosimetry (spatial resolution of 12.6 mm), with/without SC (optimized, or with parameters predefined by the manufacturer; dual energy window), and with/without AC. Activity in voxels was calculated by a relative calibration, assuming identical microspheres and 99mTc-SPECT counts spatial distribution. 3D dose distributions were calculated by convolution with 90Y voxel -values, assuming permanent trapping of microspheres. Cumulative dose-volume histograms in lesions and healthy parenchyma from different reconstructions were compared with those obtained from the reference biodistribution (the “gold standard,” GS), assessing differences for 95%, 70%, and 50% (i.e., minimum value of the absorbed dose to a percentage of the irradiated volume). tool analysis with tolerance of 3%/13 mm was used to evaluate the agreement between GS and simulated cases. The influence of deep-breathing was studied, blurring the reference biodistributions with a 3D anisotropic gaussian kernel, and performing the simulations once again.

Differences of the dosimetric indicators were noticeable in some cases, always negative for lesions and distributed around zero for parenchyma. Application of AC and SC reduced systematically the differences for lesions by 5%–14% for a liver segment, and by 7%–12% for a nonuniform liver. For parenchyma, the data trend was less clear, but the overall range of variability passed from −10%/40% for a liver segment, and −10%/20% for a nonuniform liver, to −13%/6% in both cases. Applying AC, SC with preset parameters gave similar results to optimized SC, as confirmed by tool analysis. Moreover, analysis confirmed that solely AC and SC are not sufficient to obtain accurate 3D dose distribution. With breathing, the accuracy worsened severely for all dosimetric indicators, above all for lesions: with AC and optimized SC, −38%/−13% in liver’s segment, −61%/−40% in the nonuniform liver. For parenchyma, 50% resulted always less sensitive to breathing and sub-optimal correction methods (difference overall range: −7%/13%).

Reconstruction protocol optimization, AC, SC, PVE and respiratory motion corrections should be implemented to obtain the best possible dosimetric accuracy. On the other side, thanks to the relative calibration, 50% inaccuracy for the healthy parenchyma from absence of AC was less than expected, while the optimization of SC was scarcely influent. The relative calibration therefore allows to perform TARE planning, basing on 50% for the healthy parenchyma, even without AC or with suboptimal corrections, rather than rely on nondosimetric methods.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd