Dose point kernels for 2,174 radionuclides

Prostate Cancer Radioligand Therapy: Beta-labeled Radiopharmaceuticals

Prostate cancer is the most common malignancy in men worldwide, with an estimated 174,650 new cases per year in the United States, and the second cancer-related cause of death, after lung cancer, with 31,620 deaths per year. While the 5 year survival rate for prostate cancer in patients without metastatic spread is nearly 100%, those with distant metastases have 5 year survival rates of approximately 30%. Initial diagnosis and assessment are based on PSA levels, Gleason score (derived from prostate biopsy), and advanced imaging modalities, including prostate MR imaging and PSMA-PET/computed tomography in patients with high-risk features.

Ultrasensitive and multiplexed tracking of single cells using whole-body PET/CT

In vivo molecular imaging tools are crucially important for elucidating how cells move through complex biological systems; however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upward of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 becquerel per cell and a streamlined workflow to reliably label single cells with over 50 becquerel per cell of 18F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of more than 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.

A diffusion-leakage model coupled with dose point kernels (DPK) for dosimetry of diffusing alpha-emitters radiation therapy (DaRT)

BACKGROUND

Diffusing alpha-emitters radiation therapy (DaRT) is a novel brachytherapy technique that leverages the diffusive flow of 224Ra progeny within the tumor volume over the course of the treatment. Cell killing is achieved by the emitted alpha particles that have a short range in tissue and high linear energy transfer. The current proposed absorbed dose calculation method for DaRT is based on a diffusion-leakage (DL) model that neglects absorbed dose from beta particles.

PURPOSE

This work aimed to couple the DL model with dose point kernels (DPKs) to account for dose from beta particles as well as to consider the non-local deposition of energy.

METHODS

The DaRT seed was modeled using COMSOL multiphysics and the DL model was implemented to extract the spatial information of the diffusing daughters. Using Monte-Carlo (MC) methods, DPKs were generated for 212Pb, 212Bi, and their progenies since they were considered to be the dominant beta emitters in the 224Ra radioactive decay chain. A convolution operation was performed between the integrated number densities of the diffusing daughters and DPKs to calculate the total absorbed dose over a 30-day treatment period. Both high-diffusion and low-diffusion cases were considered.

RESULTS

The calculated DPKs showed non-negligible energy deposition over several millimeters from the source location. An absorbed dose >10 Gy was deposited within a 1.8 mm radial distance for the low diffusion case and a 2.2 mm radial distance for the high diffusion case. When the DPK method was compared with the local energy deposition method that solely considered dose from alpha particles, differences above 1 Gy were found within 1.3 and 1.8 mm radial distances from the surface of the source for the low diffusion and high diffusion cases, respectively.

CONCLUSIONS

The proposed method enhances the accuracy of the dose calculation method used for the DaRT technique.

Characterization of a segmented printed circuit board (PCB) as a standard for absorbed dose to water from alpha-emitting radionuclides

BACKGROUND

Our previous work introduced and evaluated a standard for surface absorbed dose rate per unit radioactivity to water from unsealed alpha-emitting radionuclides used in targeted radionuclide therapy (TRT). An overall uncertainty over 4.0% at k = 1 was reported for the absorbed dose to air measurements, which was partially attributed to the rotational alignment uncertainty in the geometrical setup.

PURPOSE

A printed circuit board (PCB) with a segmented guard was constructed to align the extrapolation chamber (EC) and the source plates using a differential capacitance technique. The PCB EC aimed to enhance the repeatability of the ionization current measurements. The PCB EC was evaluated using a thin film 210Po source. The measured absorbed dose to air cavity was compared with the Monte Carlo (MC) calculations. Using the extrapolation method, the surface absorbed dose rate to water was calculated.

METHODS

The PCB EC was constructed with a 4.50 mm diameter collector surrounded by four sectors and a guard electrode. The sectors were isolated for rotational alignment and later connected to the guard for ionization current measurements. A bridge circuit measured differential capacitance between opposing sectors, and a hexapod motion stage rotated the source substrate to minimize the differential capacitance. The EC was evaluated using a 210Po source with a 3.20 mm diameter and 1.253 μ $\mu $ Ci radioactivity. MC simulations were performed to calculate thek p o i n t ${k}_{point}$ ,k b a c k s c a t t e r ${k}_{backscatter}$ , andk d i v ${k}_{div}$ correction factors. Ionization current measurements were performed for air gaps in the 0.3-0.525 mm range and surface absorbed dose rate to water was calculated.

RESULTS

Rotational offsets of up to 3.0° were found and the current repeatability was found to increase with the absorbed dose to air uncertainty calculated to be ∼2.0%. Using the capacitance method, the effective EC diameter was measured to be 4.53 mm. The recombination, polarity, and electrometer corrections were reported to be within 1.00% across all measurement trials. The MC-calculated correction factors were calculated to be much larger than the recombination and polarity correction factors. The averagek p o i n t ${k}_{point}$ ,k b a c k s c a t t e r ${k}_{backscatter}$ , andk d i v ${k}_{div}$ corrections were calculated to be 1.063, 0.9402, and 2.136, respectively. The MC-calculated absorbed dose to air was found to overestimate the absorbed dose by over 4.00% when compared with the measured absorbed dose to air. The surface absorbed dose rate to water was calculated to be2.304 × 10 - 6 $2.304 \times {10}^{ - 6}$ Gy/s/Bq with an overall uncertainty of 4.07%.

CONCLUSIONS

The constructed PCB EC was deemed suitable as an absorbed dose standard. A repeatable rotational alignment was achieved using the differential capacitance technique. The metal electrodes on the PCB made a difference of < 1.00% on the backscatter correction when compared to the EC comprised of polystyrene-equivalent collector. A 20% difference in the surface absorbed dose rate to water was found between the two ECs, which is attributed to the cavity diameter differences leading to different magnitudes of dose fall-off along the lateral direction.

Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy

PURPOSE

Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study.

METHODS

We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions).

RESULTS

The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses.

CONCLUSION

A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.

Linear Boltzmann equation solver for voxel-level dosimetry in radiopharmaceutical therapy: Comparison with Monte Carlo and kernel convolution

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, 177 Lu SPECT/CT, 90 Y PET/CT, and 131 I 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 90 Y where the agreement was within 15%. In the patient studies (five patients imaged after 177 Lu DOTATATE RPT, five after 90 Y SIRT, and two after 131 I 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 177 Lu, 23% for 90 Y, and 26% for 131 I. 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.

Technical note: Errors introduced when using Dose Voxel Kernels for estimating absorbed dose from radiopharmaceutical therapies involving alpha emitters

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 (177 Lu) and alphas (211 At, 225 Ac, and 227 Th) 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 (177 Lu) 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.

A machine learning-based model for a dose point kernel calculation

PURPOSE

Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study reports on the design, implementation, and test of a multi-target regressor approach to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters.

METHODS

DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Regressor Chains (RC) with three different coefficients regularization/shrinkage models were used as base regressors. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the beta emitters sDPK were applied to a patient-specific case calculating the Voxel Dose Kernel (VDK) for a hepatic radioembolization treatment with [Formula: see text]Y.

RESULTS

The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than [Formula: see text] in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than [Formula: see text] were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations.

CONCLUSION

An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required short computation times.

Internal Bremsstrahlung, the missing process in beta decay Monte Carlo simulation: The relevance in 32P Dose-Point-Kernel estimation

PURPOSE

In nuclear medicine, Dose Point Kernels (DPKs), representing the energy deposited all around a point isotropic source, are extensively used for dosimetry and are usually obtained by Monte Carlo (MC) simulations. For beta-decaying nuclides, DPK is usually estimated neglecting Internal Bremsstrahlung (IB) emission, a process always accompanying the beta decay and consisting in the emission of photons having a continuous spectral distribution. This work aims to study the significance of IB emission for DPK estimation in the case of ³²P and provide DPK values corrected for the IB photon contribution.

METHODS

DPK, in terms of the scaled absorbed dose fraction, F(R/X₉₀), was first estimated by GAMOS MC simulation using the standard beta decay spectrum of ³²P, F_β(R/X₉₀). Subsequently, an additional source term accounting for IB photons and their spectral distribution was defined and used for a further MC simulation, thus evaluating the contribution of IB emission to DPK values, F_β+IB(R/X₉₀). The relative percent difference, δ, between the DPKs obtained by the two approaches, F_β+IB vs. F_β, was studied as a function of the radial distance, R.

RESULTS

As far as the energy deposition is mainly due to the beta particles, IB photons does not significantly contribute to DPK; conversely, for larger R, F_β+IB values are higher by 30-40% than F_β.

CONCLUSIONS

The inclusion of IB emission in the MC simulations for DPK estimations is recommended, as well as the use of the DPK values corrected for IB photons, here provided.

Technical note: The contribution of internal bremsstrahlung to the 90 Y dose point kernel

BACKGROUND

Internal dosimetry has an increasing role in the planning and verification of nuclear medicine therapies with radiopharmaceuticals. Dose Point Kernels (DPKs), quantifying the energy deposition all around a point source, in a homogenous medium, are extensively used for 3D dosimetry and nowadays are mostly evaluated by Monte Carlo (MC) simulation. To our knowledge, DPK for beta emitters is estimated neglecting the continuous photon emission due to the Internal Bremsstrahlung (IB), whose contribution to the absorbed dose can be relevant beyond the maximum range of betas, as evidenced in recent works.

PURPOSE

Aim of this study was to investigate and quantify, by means of MC simulations, the contribution of IB photons to DPK calculated for ⁹⁰ Y and provide the updated ⁹⁰ Y DPK.

METHODS

The overall radiation due to the decay of a ⁹⁰ Y point source, placed at the centre of concentric water shells of increasing radii from 0.02 cm to 20 cm, was simulated with GAMOS, including the IB source term whose spectral distribution was described by an analytical model. Energy deposition was scored in the shells as a function of the distance from the source, R, and DPK was estimated in terms of the scaled absorbed dose fraction, F(R/X₉₀ ), where X₉₀ is the range within which the beta particles deposit 90% of their energy.

RESULTS

A comparison between the two simulated absorbed dose distributions, calculated with or without IB, clearly shows that the latter (incomplete) choice is consistent with the findings of other Authors and systematically underestimates the absorbed dose imparted to the tissue. ⁹⁰ Y DPK values currently used are underestimated by 20%-34% for R>2X₉₀ .

CONCLUSIONS

The revised values provided in this work suggest that the inclusion of IB emission in DPK evaluations is advisable for pure beta emitters.

A Machine Learning based model for a Dose Point Kernel calculation

PURPOSE

Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study shows applications of machine learning to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters.

METHODS

DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Three machine learning (ML) algorithms were trained using the MC DPKs. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the ML sDPK approach was applied to a patient-specific case calculating the dose voxel kernels (DVK) for a hepatic radioembolization treatment with \(^{90}\)Y.

RESULTS

The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than \(10%\) in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than \(7 %\) were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations.

CONCLUSION

An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required remarkable short computation times.

Small-scale (sub-organ and cellular level) alpha-particle dosimetry methods using an iQID digital autoradiography imaging system

Targeted radiopharmaceutical therapy with alpha-particle emitters (αRPT) is advantageous in cancer treatment because the short range and high local energy deposition of alpha particles enable precise radiation delivery and efficient tumor cell killing. However, these properties create sub-organ dose deposition effects that are not easily characterized by direct gamma-ray imaging (PET or SPECT). We present a computational procedure to determine the spatial distribution of absorbed dose from alpha-emitting radionuclides in tissues using digital autoradiography activity images from an ionizing-radiation quantum imaging detector (iQID). Data from ²¹¹At-radioimmunotherapy studies for allogeneic hematopoietic cell transplantation in a canine model were used to develop these methods. Nine healthy canines were treated with 16.9-30.9 MBq ²¹¹At/mg monoclonal antibodies (mAb). Lymph node biopsies from early (2-5 h) and late (19-20 h) time points (16 total) were obtained, with 10-20 consecutive 12-µm cryosections extracted from each and imaged with an iQID device. iQID spatial activity images were registered within a 3D volume for dose-point-kernel convolution, producing dose-rate maps. The accumulated absorbed doses for high- and low-rate regions were 9 ± 4 Gy and 1.2 ± 0.8 Gy from separate dose-rate curves, respectively. We further assess uptake uniformity, co-registration with histological pathology, and requisite slice numbers to improve microscale characterization of absorbed dose inhomogeneities in αRPT.

Comparison of Voxel S-Value Methods for Personalized Voxel-based Dosimetry of 177 Lu-DOTATATE

PURPOSE

Voxel-based dosimetry is potentially accurate than organ-based dosimetry because it considers the anatomical variations in each individual and the heterogeneous radioactivity distribution in each organ. Here, voxel-based dosimetry for ¹⁷⁷ Lu-DOTATATE therapy was performed using single and multiple voxel S-value (VSV) methods and compared with Monte Carlo simulations. To verify these methods, we adopted sequential ¹⁷⁷ Lu-DOTATATE SPECT/CT dataset acquired from Sunway Medical Centre using the major vendor's SPECT/CT scanner (Siemens) METHODS: The administered activity of ¹⁷⁷ Lu-DOTATATE was 7.99 ± 0.36 GBq. SPECT/CT images were acquired 0.5, 4, 24, and 48 h after injection in Sunway Medical Centre. For the multiple VSV method VSV kernels of ¹⁷⁷ Lu in media with various densities were generated by GATE simulation first. The second step involved the convolution of the time-integrated activity map with each kernel to produce medium-specific dose maps. Third, each medium-specific dose map was masked using binary medium masks, which were generated from CT-based density maps. Finally, all masked dose maps were summed to generate the final dose map. VSV methods with four different VSV sets (1, 4, 10, and 20 VSVs) were compared. Voxel-wise density correction for the single VSV method was also performed. The absorbed doses in the kidneys, bone marrow, and tumors were analyzed, and the relative errors between the VSV and Monte Carlo simulation approaches were estimated. Organ-based dosimetry using OLINDA/EXM was also compared RESULTS: The accuracy of the multiple VSV approach increased with the number of dose kernels. The average dose estimation errors of a single VSV with density correction and 20 VSVs were less than 6% in most cases, although organ-based dosimetry using OLINDA/EXM yielded an error of up to 123%. The advantages of the single VSV method with density correction and the 20 VSVs over organ-based dosimetry were most evident in bone marrow and bone-metastatic tumors with heterogeneous medium properties.

CONCLUSION

The single VSV method with density correction and multiple VSV method with 20 dose kernels enabled fast and accurate radiation dose estimation. Accordingly, voxel-based dosimetry methods can be useful for managing administration activity and for investigating tumor dose responses to further increase the therapeutic efficacy of ¹⁷⁷ Lu-DOTATATE. This article is protected by copyright. All rights reserved.

Quantitative SPECT/CT for dosimetry of peptide receptor radionuclide therapy

Neuroendocrine tumors (NETs) are uncommon malignancies of increasing incidence and prevalence. As these slow growing tumors usually overexpress somatostatin receptors (SSTRs), the use of ⁶⁸Ga-DOTA-peptides (gallium-68 chelated with dodecane tetra-acetic acid to somatostatin), which bind to the SSTRs, allows for PET based imaging and selection of patients for peptide receptor radionuclide therapy (PRRT). PRRT with radiolabeled somatostatin analogues such as ¹⁷⁷Lu-DOTATATE (lutetium-177-[DOTA,Tyr³]-octreotate), is mainly used for the treatment of metastatic or inoperable NETs. However, PRRT is generally administered at a fixed injected activity in order not to exceed dose limits in critical organs, which is suboptimal given the variability in radiopharmaceutical uptake among patients. Advances in SPECT (single photon emission computed tomography) imaging enable the absolute quantitative measure of the true radiopharmaceutical distribution providing for PRRT dosimetry in each patient. Personalized PRRT based on patient-specific dosimetry could improve therapeutic efficacy by optimizing effective tumor absorbed dose while limiting treatment related radiotoxicity.

Dosimetry for Radiopharmaceutical Therapy: Current Practices and Commercial Resources

With the ongoing dramatic growth of radiopharmaceutical therapy, research and development in internal radiation dosimetry continue to advance both at academic medical centers and in industry. The basic paradigm for patient-specific dosimetry includes administration of a pretreatment tracer activity of the therapeutic radiopharmaceutical; measurement of its time-dependent biodistribution; definition of the pertinent anatomy; integration of the measured time-activity data to derive source-region time-integrated activities; calculation of the tumor, organ-at-risk, and/or whole-body absorbed doses; and prescription of the therapeutic administered activity. This paper provides an overview of the state of the art of patient-specific dosimetry for radiopharmaceutical therapy, including current methods and commercially available software and other resources.

Imaging and dosimetric characteristics of 67 Cu

In recent years the use of beta-emitting radiopharmaceuticals for cancer therapy has expanded rapidly following development of therapeutics for neuroendocrine tumors, prostate cancer, and other oncologic malignancies. One emerging beta-emitting radioisotope of interest for therapy is ⁶⁷Cu (t_1/2: 2.6 d) due to its chemical equivalency with the widely-established positron-emitting isotope ⁶⁴Cu (t_1/2: 12.7 h). In this work we evaluate both the imaging and dosimetric characteristics of ⁶⁷Cu, as well as producing the first report of SPECT/CT imaging using ⁶⁷Cu. To this end, ⁶⁷Cu was produced by photon-induced reactions on isotopically-enriched ⁶⁸Zn at the Low-Energy Accelerator Facility (LEAF) of Argonne National Laboratory, followed by bulk separation of metallic ⁶⁸Zn by sublimation and radiochemical purification by column chromatography. Gamma spectrometry was performed by efficiency-calibrated high-purity germanium (HPGe) analysis to verify absolute activity calibration and establish radionuclidic purity. Absolute activity measurements corroborated manufacturer-recommended dose-calibrator settings and no radionuclidic impurities were observed. Using the Clinical Trials Network anthropomorphic chest phantom, SPECT/CT images were acquired. Medium energy (ME) SPECT collimation was found to provide the best image quality from the primary 185 keV gamma emission of ⁶⁷Cu. Reconstructed images of ⁶⁷Cu were similar in quality to images acquired using ¹⁷⁷Lu. Recovery coefficients were calculated and compared against quantitative images of ^99mTc, ¹⁷⁷Lu, and ⁶⁴Cu within the same anthropomorphic chest phantom. Production and clinical imaging of ⁶⁷Cu appears feasible, and future studies investigating the therapeutic efficacy of ⁶⁷Cu-based radiopharmaceuticals are warranted.

Dosimetry for Optimized, Personalized Radiopharmaceutical Therapy

Radiopharmaceutical therapy (RPT) has grown rapidly over the last decade for treatment of numerous cancer types. Dosimetric guidance, as with other radiotherapy modalities, has benefitted patients by reducing the incidence of side effects and improving overall survival in populations treated under this paradigm. Development of tools and techniques for dosimetry-guided therapy is ongoing, with numerous the Food and Drug Administration-cleared products reaching the U.S. market in 2019. Safe use of commercial dosimetry platforms requires a deep understanding of the underlying physical principles and thoroughly vetted input data. Likewise, interpretation of dosimetry results relies on an understanding of radiobiological principles, and the principles of uncertainty propagation. In this article, we review strategies commonly employed for dosimetry-guided RPT - including quantitative imaging, dose calculation methods, and modeling of dose across time-points. Additionally, we review recent literature evidence (2013-2020) demonstrating the efficacy of personalized RPT.

A Monte Carlo Investigation of Dose Point Kernel Scaling for α-Emitting Radionuclides

Background: Density-based dose point kernel (DPK) scaling accuracy was investigated in various homogeneous tissue media. Methods: Using GEometry ANd Tracking 4 Monte Carlo code, DPKs were generated for 5, 8 MeV monoenergetic α particles and ²²³Ra, ²²⁵Ac, and ²²⁷Th. Dose was scored in 1 μm thick concentric shells and DPKs were scaled based on the tissue's mass density and compared with the water DPK. Results: Scaled kernels agreed within ±5% except near the Bragg peaks, where they differed up to 25%. Conclusions: The authors conclude that kernel scaling based on mass density of the transport medium can be utilized accurately up to 5%, excluding Bragg peak regions.

Absorbed dose distributions from beta-decaying radionuclides: Experimental validation of Monte Carlo tools for radiopharmaceutical dosimetry

PURPOSE

This study aims to experimentally validate the Monte Carlo generated absorbed doses from the beta particles emitted by ⁹⁰ Y and ¹⁷⁷ Lu using radiochromic EBT3 film-based dosimetry.

METHODS

Line sources of ⁹⁰ Y and ¹⁷⁷ Lu were inserted longitudinally through blocks of low-density polyethylene and tissue-equivalent slabs of cortical bone and lung equivalent plastics. Radiochromic film (Gafchromic EBT3) was laser cut to accommodate orthogonal line sources of radioactivity, and the film was sandwiched intimately between the rectangular blocks to achieve charged particle equilibrium. Line sources consisted of plastic capillary tube of length (13 ± 0.1) cm, with 0.42-mm inner diameter and a wall thickness of 0.21 mm. ⁹⁰ Y line sources were prepared from a solution of dissolved ⁹⁰ Y resin microspheres. ¹⁷⁷ Lu line sources were prepared from an aliquot of ¹⁷⁷ Lu-DOTATATE. Film exposures were conducted for durations ranging from 10 min to 38 h. Radiochromic film calibration was performed by irradiation with 6-MV-bremsstrahlung x rays from a calibrated linear accelerator, in accordance with literature recommendations. Experimental geometries were precisely simulated within the GATE Monte Carlo toolkit, which has previously been used for the generation of dose point kernels.

RESULTS

The mean percentage difference between measured and simulated absorbed doses were 5.04% and 7.21% for ⁹⁰ Y and ¹⁷⁷ Lu beta absorbed dose in the range of (0.1-10) Gy. Additionally, 1D gamma analysis using a local 10%/1 mm gamma criterion was performed to compare the absorbed dose distributions. The percentage of measurement points passing the gamma criterion, averaged over all tests, was 93.5%.

CONCLUSIONS

We report the experimental validation of Monte Carlo derived beta absorbed dose distributions for ⁹⁰ Y and ¹⁷⁷ Lu, solidifying the validity of using Monte Carlo-based methods for estimating absorbed dose from beta emitters. Overall, excellent agreement was observed between the experimental beta absorbed doses in the linear region of the radiochromic film and the GATE Monte Carlo simulations demonstrating that radiochromic film dosimetry has sufficient sensitivity and spatial resolution to be used as a tool for measuring beta decay absorbed dose distributions.

The Impact of Tissue Type and Density on Dose Point Kernels for Patient-Specific Voxel-Wise Dosimetry: A Monte Carlo Investigation

We report the generation of dose point kernels for clinically-relevant radionuclide beta decays and monoenergetic electrons in various tissues to understand the impact of tissue type on dose point kernels. Currently available voxel-wise dosimetry approaches using dose point kernels ignore tissue composition and density heterogeneities. Therefore, the study on the impact of tissue type on dose point kernels is warranted. Simulations were performed using the GATE Monte Carlo toolkit, which encapsulates GEANT4 libraries. Dose point kernels were simulated in phantoms of water, compact bone, lung, adipose tissue, blood and red marrow for radionuclides 90Y, 188Re, 32P, 89Sr, 186Re, 153Sm and 177Lu and monoenergetic electrons (0.015-10 MeV). All simulations were performed by assuming an isotropic point source of electrons at the center of a homogeneous spherical phantom. Tissue-specific differences between kernels were investigated by normalizing kernels for effective pathlength. Transport of 20 million particles was found to provide sufficient statistical precision in all simulated kernels. The simulated dose point kernels demonstrate excellent agreement with other Monte Carlo packages. Deviation from kernels reported in the literature did not exceed a 10% global difference, which is consistent with the variability among published results. There are no significant differences between the dose point kernel in water and kernels in other tissues that have been scaled to account for density; however, tissue density predictably demonstrated itself to be a significant variable in dose point kernel distribution.