Purpose: Virtual dosimetry using voxel-based patient-specific phantoms and Monte Carlo (MC) simulations offer the advantage of having a gold standard against which absorbed doses may be benchmarked to establish the dosimetry accuracy. Furthermore, these reference values assist in investigating the accuracy of the absorbed dose methodologies from different software programs. Therefore, this study aimed to compare the accuracy of the absorbed doses computed using LundADose and OLINDA/EXM 1.0.
Methods: The accuracy was based on Lu-DOTATATE distributions of three voxel-based phantoms. SPECT projection images were simulated for 1, 24, 96, and 168 h post-administration and reconstructed with LundADose using 3D OS-EM reconstruction. Mono-exponential curves were fitted to the bio-kinetic data for the kidneys, liver, spleen, and tumours resulting in SPECT time-integrated activity (SPECT-TIA). The SPECT-TIA were used to compute mean absorbed doses using LundADose (LND-D) and OLINDA (OLINDA-D) for the organs. Pre-defined true activity images, were used to obtain TRUE-TIA and, together with full MC simulations, computed the true doses (MC-D). The dosimetry accuracy was assessed by comparing LND-D and OLINDA-D to MC-D.
Results: Overall, the results presented an overestimation of the mean absorbed dose by LND-D compared to the MC-D with a dosimetry accuracy ≤6.6%. This was attributed to spill-out activity from the reconstructed LND-D, resulting in a higher dose contribution than the MC-D. There was a general underestimation (<8.1%) of OLINDA-D compared to MC-D attributed to the geometrical difference in shape between the voxel-based phantoms and the OLINDA models. Furthermore, OLINDA-D considers self-doses while MC-D reflects self-doses plus cross-doses.
Conclusion: The better than 10% accuracy suggests that the mean dose values obtained with LND-D and OLINDA-D approximate the true values. The mean absorbed doses of the two software programs, and the gold standard were comparable. This work shall be of use for optimising Lu dosimetry for clinical applications.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293745 | PMC |
http://dx.doi.org/10.1016/j.heliyon.2022.e09830 | DOI Listing |
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