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Background: With recent interest in patient‐specific dosimetry for radiopharmaceutical therapy (RPT) and selective internal radiation therapy (SIRT), an increasing number of voxel‐based algorithms are being evaluated. Monte Carlo (MC) radiation transport, generally considered to be the most accurate among different methods for voxel‐level absorbed dose estimation, can be computationally inefficient for routine clinical use. Purpose: This work demonstrates a recently implemented grid‐based linear Boltzmann transport equation (LBTE) solver for fast and accurate voxel‐based dosimetry in RPT and SIRT and benchmarks it against MC. Methods: A deterministic LBTE solver (Acuros MRT) was implemented within a commercial RPT dosimetry package (Velocity 4.1). The LBTE is directly discretized using an adaptive mesh refined grid and then the coupled photon‐electron radiation transport is iteratively solved inside specified volumes to estimate radiation doses from both photons and charged particles in heterogeneous media. To evaluate the performance of the LBTE solver for RPT and SIRT applications, 177Lu SPECT/CT, 90Y PET/CT, and 131I SPECT/CT images of phantoms and patients were used. Multiple lesions (2–1052 mL) and normal organs were delineated for each study. Voxel dosimetry was performed with the LBTE solver, dose voxel kernel (DVK) convolution with density correction, and a validated in‐house MC code using the same time‐integrated activity and density maps as input to the different dose engines. The resulting dose maps, difference maps, and dose‐volume‐histogram (DVH) metrics were compared, to assess the voxel‐level agreement. Evaluation of mean absorbed dose included comparison with structure‐level estimates from OLINDA. Results: In the phantom inserts/compartments, the LBTE solver versus MC and DVK convolution demonstrated good agreement with mean absorbed dose and DVH metrics agreeing to within 5% except for the D90 and D70 metrics of a very low activity concentration insert of 90Y where the agreement was within 15%. In the patient studies (five patients imaged after 177Lu DOTATATE RPT, five after 90Y SIRT, and two after 131I radioimmunotherapy), in general, there was better agreement between the LBTE solver and MC than between LBTE solver and DVK convolution for mean absorbed dose and voxel‐level evaluations. Across all patients for all three radionuclides, for soft tissue structures (kidney, liver, lesions), the mean absorbed dose estimates from the LBTE solver were in good agreement with those from MC (median difference < 1%, maximum 9%) and those from DVK (median difference < 5%, maximum 9%). The LBTE and OLINDA estimates for mean absorbed dose in kidneys and liver agreed to within 10%, but differences for lesions were larger with a maximum 14% for 177Lu, 23% for 90Y, and 26% for 131I. For bone regions, the agreement in mean absorbed doses between LBTE and both MC and DVK were similar (median < 11%, max 11%) while for lung the agreement between LBTE and MC (median < 1%, max 8%) was substantially better than between LBTE and DVK (median < 16%, max 33%). Voxel level estimates for soft tissue structures also showed good agreement between the LBTE solver and both MC and DVK with a median difference < 5% (maximum < 13%) for the DVH metrics with all three radionuclides. The largest difference in DVH metrics was for the D90 and D70 metric in lung and bone where the uptake was low. Here, the difference between LBTE and MC had a median value < 14% (maximum 23%) for bone and < 4% (maximum 37%) for lung, while the corresponding differences between LBTE and DVK were < 23% (maximum 31%) and < 67% (maximum 313%), respectively. For a typical patient with a matrix size of 166 × 166 × 129 (voxel size 3 × 3 × 3 mm3), voxel dosimetry using the LBTE solver was as fast as ∼2 min on a desktop computer. Conclusion: Having established good agreement between the LBTE solver and MC for RPT and SIRT applications, the LBTE solver is a viable option for voxel dosimetry that can be faster than MC. Further analysis is being performed to encompass the broad range of radionuclides and conditions encountered clinically. [ABSTRACT FROM AUTHOR]

Background: In radiopharmaceutical therapies (RPT) involving beta emitters, absorbed dose (Dabs) calculations often employ the use of dose voxel kernels (DVK). Such methods are faster and easier to implement than Monte Carlo (MC) simulations. Using DVK methods implies a non‐stochastic distribution of particles. This is a valid assumption for betas where thousands to tens of thousands of particles traversing the cell nucleus are required to achieve cell kill. However, alpha particles have linear energy transfers (LET) that are ∼500 times higher than LETs of betas. This results in a significant probability of killing a cell from even a single traversal through its nucleus. Consequently, the activity used for therapy involving alphas is very low, and the use of DVKs for estimating Dabs will generate results that may be erroneous. Purpose: This work aims at illustrating how use of DVKs affect the resulting Dabs in small tumors when irradiated with clinically relevant amounts of beta‐ and alpha‐emitters. The results are compared with those from using a Monte Carlo method where the energy deposition from individual tracks is simulated. Methods: To illustrate the issues associated with DVK for alpha radiopharmaceutical therapies at the microscale, a tumor cluster model was used to compare beta (177Lu) and alphas (211At, 225Ac, and 227Th) irradiations. We used 103 beta particles and 20 alpha particles per cell, which is within the range of the required number of particle traversals through its nucleus to sterilize a cell. Results from using both methods were presented with Dabs histograms, dose volume histograms, and Dabs error maps. Results: For beta‐emitter (177Lu) irradiating the modeled tumor cluster, resulting Dabs was similar for both DVK and MC methods. For all alpha emitters, the use of DVK led to an overestimation of Dabs when compared to results generated using a MC approach. Conclusions: Our results demonstrate that the use of DVK methods for alpha emitters can lead to an overestimation in the calculated Dabs. The use of DVKs for therapies involving alpha emitters may therefore not be appropriate when only referring to the mean Dabs metric. [ABSTRACT FROM AUTHOR]

Background: Dosimetry software tools developed for Radiopharmaceutical Therapy, such as OLINDA/EXM or IDAC‐Dose, account only for radiation dose to organs from radiopharmaceutical taken up in other organs. Purpose: The aim of this study is to present a methodology, that can be applied to any voxelised computational model, able to account for cross‐dose to organs from tumors of any shape and number enclosed within an organ. Methods: A Geant4 application using hybrid analytical/voxelised geometries has been developed as an extension to the ICRP110_HumanPhantom Geant4 advanced example and validated against ICRP publication 133. In this new Geant4 application, tumors are defined using the Geant4 Parallel Geometry functionality, which allows the co‐existence of two independent geometries in the same Monte Carlo simulation. The methodology was validated by estimating total dose to healthy tissue from 90Y and from 177Lu distributed within tumors of various sizes localized within the liver of the ICRP110 adult male phantom. Results: Agreement of the Geant4 application with ICRP133 was within 5% when masses were adjusted for blood content. Total dose to healthy liver and to tumors was found to agree within 1% when compared to the ground truth. Conclusions: The methodology presented in this work can be extended to investigate total dose to healthy tissue from systemic uptake of radiopharmaceuticals in tumors of different sizes using any voxelised computational dosimetric model. [ABSTRACT FROM AUTHOR]

Background: Recent developments in alpha and beta emitting radionuclide therapy highlight the importance of developing efficient methods for patient‐specific dosimetry. Traditional tabulated methods such as Medical Internal Radiation Dose (MIRD) estimate the dose at the organ level while more recent numerical methods based on Monte Carlo (MC) simulations are able to calculate dose at the voxel level. A precalculated MC (PMC) approach was developed in this work as an alternative to time‐consuming fully simulated MC. Once the spatial distribution of alpha and beta emitters is determined using imaging and/or numerical methods, the PMC code can be used to achieve an accurate voxelized 3D distribution of the deposited energy without relying on full MC calculations. Purpose: To implement the PMC method to calculate energy deposited by alpha and beta particles emitted from the Ra‐224 decay chain. Methods: The GEANT4 (version 10.7) MC toolkit was used to generate databases of precalculated tracks to be integrated in the PMC code as well as to benchmark its output. In this regard, energy spectra of alpha and beta particles emitted by the Ra‐224 decay chain were generated using GAMOS (version 6.2.0) and imported into GEANT4 macro files. Either alpha or beta emitting sources were defined at the center of a homogeneous phantom filled with various materials such as soft tissue, bone, and lung where particles were emitted either mono‐directionally (for database generation) or isotropically (for benchmarking). Two heterogeneous phantoms were used to demonstrate PMC code compatibility with boundary crossing events. Each precalculated database was generated step‐by‐step by storing particle track information from GEANT4 simulations followed by its integration in a PMC code developed in MATLAB. For a user‐defined number of histories, one of the tracks in a given database was selected randomly and rotated randomly to reflect an isotropic emission. Afterward, deposited energy was divided between voxels based on step length in each voxel using a ray‐tracing approach. The radial distribution of deposited energy was benchmarked against fully simulated MC calculations using GEANT4. The effect of the GEANT4 parameter StepMax on the accuracy and speed of the code was also investigated. Results: In the case of alpha decay, primary alpha particles show the highest contribution (>99%) in deposited energy compared to their secondary particles. In most cases, protons act as the main secondary particles in the deposition of energy. However, for a lung phantom, using a range cutoff parameter of 10 µm on primary alpha particles yields a higher contribution of secondary electrons than protons. Differences between deposited energy calculated by PMC and fully simulated MC are within 2% for all alpha and beta emitters in homogeneous and heterogeneous phantoms. Additionally, statistical uncertainties are less than 1% for voxels with doses higher than 5% of the maximum dose. Moreover, optimization of the parameter StepMax is necessary to achieve the best tradeoff between code accuracy and speed. Conclusions: The PMC code shows good performance for dose calculations deposited by alpha and beta emitters. As a stand‐alone algorithm, it is suitable to be integrated into clinical treatment planning systems. [ABSTRACT FROM AUTHOR]

Purpose: Validation of dosimetry software, such as Monte Carlo (MC) radiation transport codes used for patient‐specific absorbed dose estimation, is critical prior to their use in clinical decision making. However, direct experimental validation in the clinic is generally not performed for low/medium‐energy beta emitters used in radiopharmaceutical therapy (RPT) due to the challenges of measuring energy deposited by short‐range particles. Our objective was to design a practical phantom geometry for radiochromic film (RF)‐based absorbed dose measurements of beta‐emitting radionuclides and perform experiments to directly validate our in‐house developed Dose Planning Method (DPM) MC code dedicated to internal dosimetry. Methods: The experimental setup was designed for measuring absorbed dose from beta emitters that have a range sufficiently penetrating to ∼200 μm in water as well as to capture any photon contributions to absorbed dose. Assayed 177Lu and 90Y liquid sources, 13–450 MBq estimated to deliver 0.5–10 Gy to the sensitive layer of the RF, were injected into the cavity of two 3D‐printed half‐cylinders that had been sealed with 12.7 μm or 25.4 μm thick Kapton Tape. A 3.8 × 6 cm strip of GafChromic EBT3 RF was sandwiched between the two taped half‐cylinders. After 2–48 h exposures, films were retrieved and wipe tested for contamination. Absorbed dose to the RF was measured using a commercial triple‐channel dosimetry optimization method and a calibration generated via 6 MV photon beam. Profiles were analyzed across the central 1 cm2 area of the RF for validation. Eleven experiments were completed with 177Lu and nine with 90Y both in saline and a bone equivalent solution. Depth dose curves were generated for 177Lu and 90Y stacking multiple RF strips between a single filled half‐cylinder and an acrylic backing. All experiments were modeled in DPM to generate voxelized MC absorbed dose estimates. We extended our study to benchmark general purpose MC codes MCNP6 and EGSnrc against the experimental results as well. Results: A total of 20 experiments showed that both the 3D‐printed phantoms and the final absorbed dose values were reproducible. The agreement between the absorbed dose estimates from the RF measurements and DPM was on average −4.0% (range −10.9% to 3.2%) for all single film 177Lu experiments and was on average −1.0% (range −2.7% to 0.7%) for all single film 90Y experiments. Absorbed depth dose estimates by DPM agreed with RF on average 1.2% (range −8.0% to 15.2%) across all depths for 177Lu and on average 4.0% (range −5.0% to 9.3%) across all depths for 90Y. DPM absorbed dose estimates agreed with estimates from EGSnrc and MCNP across the board, within 4.7% and within 3.4% for 177Lu and 90Y respectively, for all geometries and across all depths. MC showed that absorbed dose to RF from betas was greater than 92% of the total (betas + other radiations) for 177Lu, indicating measurement of dominant beta contribution with our design. Conclusions: The reproducible results with a RF insert in a simple phantom designed for liquid sources demonstrate that this is a reliable setup for experimentally validating dosimetry algorithms used in therapies with beta‐emitting unsealed sources. Absorbed doses estimated with the DPM MC code showed close agreement with RF measurement and with results from two general purpose MC codes, thereby validating the use of this algorithms for clinical RPT dosimetry. [ABSTRACT FROM AUTHOR]