Dose Voxel Kernel Research Articles

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

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. 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. 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 (2-1052mL) 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. 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. 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.

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. 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. 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. 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. 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.

Pre-treatment dosimetry in 90Y-SIRT: Is it possible to optimise SPECT reconstruction parameters and calculation methods for accurate dosimetry?

Purpose:The aim of this study was (a) to optimise the99mTc-SPECT reconstruction parameters for the pre-treatment dosimetry of90Y-selective internal radiation therapy (SIRT) and (b) to compare the accuracy of clinical dosimetry methods with full Monte-Carlo dosimetry (fMCD) performed with Gate. Methods:To optimise the reconstruction parameters, two hundred reconstructions with different parameters were performed on a NEMA phantom, varying the number of iterations, subsets, and post-filtering. The accuracy of the dosimetric methods was then investigated using an anthropomorphic phantom. Absorbed dose maps were generated using (1) the Partition Model (PM), (2) the Dose Voxel Kernel (DVK) convolution, and (3) the Local Deposition Method (LDM) with known activity restricted to the whole phantom (WP) or to the liver and lungs (LL). The dose to the lungs was calculated using the “multiple DVK” and “multiple LDM” methods. Results:Optimal OSEM reconstruction parameters were found to depend on object size and dosimetric criterion chosen (Dmean or DVH-derived metric). The Dmean of all three dosimetric methods was close (≤ 10%) to the Dmean of fMCD simulations when considering large segmented volumes (whole liver, normal liver). In contrast, the Dmean to the small volume (∅=31) was systemically underestimated (12%–25%). For lungs, the “multiple DVK” and “multiple LDM” methods yielded a Dmean within 20% for the WP method and within 10% for the LL method. Conclusions:All three methods showed a substantial degradation of the dose-volume histograms (DVHs) compared to fMCD simulations. The DVK and LDM methods performed almost equally well, with the “multiple DVK” method being more accurate in the lungs.

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

PurposeAbsorbed 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.MethodsDPK 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 ^{90}Y.ResultsThe 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.ConclusionAn 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.

Validation of a commercial software dose calculation for Y-90 microspheres

PURPOSESeveral new commercial software packages have become available that can calculate the tumor and normal tissue dose distributions from post-treatment PET-CT scans for Y-90 microsphere treatments of liver lesions. This work seeks to validate the MIM SurePlan Liver Y90 software by comparing its results to a previously developed Monte Carlo derived voxel dose kernel calculation method. METHODSWe analyzed 10 patients who had treatments for metastatic liver cancer and created contours on post Y-90 treatment PET-CT images. We then performed dose calculations using three methods and compared the results. The first two methods calculated the dose using MIM SurePlan Liver Y90’s LDM (Local Deposition Method) and the VSV (Voxel S Value) algorithms. The third method calculated the dose using a publicly available Fluka Monte Carlo-derived dose kernel (MCK) calculation (used as ground truth). We investigated 3D Gamma passing rates and several dosimetric parameters. RESULTSA total of 3%/3 mm 3D gamma passing rates averaged 99.3% for the VSV and 78.9% for LDM. Compared to the MCK distribution, the differences for combined target GTV V70Gy and normal liver and/or lobe mean doses were small. Larger differences were seen in GTV mean doses and D95, likely due to large dose gradients in the treated regions combined with differences in dose kernel, dose grid and finite volume effects. CONCLUSIONSThe MIM SurePlan Liver Y90 VSV algorithm agreed well with the MCK calculation for patients treated with Y-90 microspheres based on the gamma analysis and several dosimetric parameters. Larger dosimetric differences in lesion mean doses and D95 suggests that these metrics are less robust to changes in calculation grid location and finite volume effects for small lesions.

Precision dosimetry in yttrium-90 radioembolization through CT imaging of radiopaque microspheres in a rabbit liver model

PurposeTo perform precision dosimetry in yttrium-90 radioembolization through CT imaging of radiopaque microspheres in a rabbit liver model and to compare extracted dose metrics to those produced from conventional PET-based dosimetry.Materials and methodsA CT calibration phantom was designed containing posts with nominal microsphere concentrations of 0.5 mg/mL, 5.0 mg/mL, and 25.0 mg/mL. The mean Hounsfield unit was extracted from the post volumes to generate a calibration curve to relate Hounsfield units to microsphere concentration. A nominal bolus of 40 mg of microspheres was administered to the livers of eight rabbits, followed by PET/CT imaging. A CT-based activity distribution was calculated through the application of the calibration curve to the CT liver volume. Post-treatment dosimetry was performed through the convolution of yttrium-90 dose-voxel kernels and the PET- and CT-based cumulated activity distributions. The mean dose to the liver in PET- and CT-based dose distributions was compared through linear regression, ANOVA, and Bland–Altman analysis.ResultsA linear least-squares fit to the average Hounsfield unit and microsphere concentration data from the calibration phantom confirmed a strong correlation (r2 > 0.999) with a slope of 14.13 HU/mg/mL. A poor correlation was found between the mean dose derived from CT and PET (r2 = 0.374), while the ANOVA analysis revealed statistically significant differences (p < 10−12) between the MIRD-derived mean dose and the PET- and CT-derived mean dose. Bland–Altman analysis predicted an offset of 15.0 Gy between the mean dose in CT and PET. The dose within the liver was shown to be more heterogeneous in CT than in PET with an average coefficient of variation equal to 1.99 and 1.02, respectively.ConclusionThe benefits of a CT-based approach to post-treatment dosimetry in yttrium-90 radioembolization include improved visualization of the dose distribution, reduced partial volume effects, a better representation of dose heterogeneity, and the mitigation of respiratory motion effects. Post-treatment CT imaging of radiopaque microspheres in yttrium-90 radioembolization provides the means to perform precision dosimetry and extract accurate dose metrics used to refine the understanding of the dose–response relationship, which could ultimately improve future patient outcomes.

DblurDoseNet: A deep residual learning network for voxel radionuclide dosimetry compensating for single-photon emission computerized tomography imaging resolution.

Current methods for patient-specific voxel-level dosimetry in radionuclide therapy suffer from a trade-off between accuracy and computational efficiency. Monte Carlo (MC) radiation transport algorithms are considered the gold standard for voxel-level dosimetry but can be computationally expensive, whereas faster dose voxel kernel (DVK) convolution can be suboptimal in the presence of tissue heterogeneities. Furthermore, the accuracies of both these methods are limited by the spatial resolution of the reconstructed emission image. To overcome these limitations, this paper considers a single deep convolutional neural network (CNN) with residual learning (named DblurDoseNet) that learns to produce dose-rate maps while compensating for the limited resolution of SPECT images. We trained our CNN using MC-generated dose-rate maps that directly corresponded to the true activity maps in virtual patient phantoms. Residual learning was applied such that our CNN learned only the difference between the true dose-rate map and DVK dose-rate map with density scaling. Our CNN consists of a 3D depth feature extractor followed by a 2D U-Net, where the input was 11 slices (3.3cm) of a given Lu-177 SPECT/CT image and density map, and the output was the dose-rate map corresponding to the center slice. The CNN was trained with nine virtual patient phantoms and tested on five different phantoms plus 42 SPECT/CT scans of patients who underwent Lu-177 DOTATATE therapy. When testing on virtual patient phantoms, the lesion/organ mean dose-rate error and the normalized root mean square error (NRMSE) relative to the ground truth of the CNN method was consistently lower than DVK and MC, when applied to SPECT images. Compared to DVK/MC, the average improvement for the CNN in mean dose-rate error was 55%/53% and 66%/56%; and in NRMSE was 18%/17% and 10%/11% for lesion and kidney regions, respectively. Line profiles and dose-volume histograms demonstrated compensation for SPECT resolution effects in the CNN-generated dose-rate maps. The ensemble noise standard deviation, determined from multiple Poisson realizations, was improved by 21%/27% compared to DVK/MC. In patients, potential improvements from CNN dose-rate maps compared to DVK/MC were illustrated qualitatively, due to the absence of ground truth. The trained residual CNN took about 30 s on a single GPU (Tesla V100) to generate a 512 512 130 dose-rate map for a patient. The proposed residual CNN, trained using phantoms generated from patient images, has potential for real-time patient-specific dosimetry in clinical treatment planning due to its demonstrated improvement in accuracy, resolution, noise, and speed over the DVK/MC approaches.

Overview of commercial treatment planning systems for targeted radionuclide therapy.

Targeted Radionuclide Therapy (TRT) is a branch of cancer medicine dealing with the therapeutic use of radioisotopes associated with biological vectors accumulating in the tumors/targets, indicated as Molecular Radiotherapy (MRT), or directly injected into the arteries that supply blood to liver tumour vasculature, indicated as Selective RT (SRT). The aim of this work is to offer a panoramic view on the increasing number of commercially-available TRT treatment planning systems (TPSs). A questionnaire was sent to manufacturers' representatives. Academic software were not considered. Questions were grouped as follows: general information, clinical workflow, calibration procedure, image processing/reconstruction, image registration and segmentation tools, time-activity curve (TAC) fitting and absorbed dose calculation. All software reported have CE-marking. TPSs were divided between SRT-dedicated software [4] and MRT [5] dosimetry software. In SRT, since no kinetic process is involved, absorbed dose calculation does not require TAC fitting, and image registration is not fully developed in all TPS. All software requires a radionuclide-specific calibration. In SRT, a relative image calibration can be obtained by scaling the counts to a known activity. Automated VOI contouring and rigid/deformable propagation between different acquisitions time-points is implemented in most TPSs, although DICOM export is rare. Different TAC fits are available depending on the number of time-points. Voxel S-value and Local deposition methods are the most frequent dosimetric approaches; dose-voxel kernel convolution and semi-Monte Carlo method are also available. Available TPSs allows performing personalized dosimetry in clinical practice. Individual variations in methodology/algorithms must be considered in the standardisation/harmonization processes.

Post-administration dosimetry in yttrium-90 radioembolization through micro-CT imaging of radiopaque microspheres in a porcine renal model

The purpose of this study is to perform post-administration dosimetry in yttrium-90 radioembolization through micro-CT imaging of radiopaque microsphere distributions in a porcine renal model and explore the impact of spatial resolution of an imaging system on the extraction of specific dose metrics. Following the administration of radiopaque microspheres to the kidney of a hybrid farm pig, the kidney was explanted and imaged with micro-CT. To produce an activity distribution, 400 MBq of yttrium-90 activity was distributed throughout segmented voxels of the embolized vasculature based on an established linear relationship between microsphere concentration and CT voxel value. This distribution was down-sampled to coarser isotropic grids ranging in voxel size from 2.5 to 15 mm to emulate nominal resolutions comparable to those found in yttrium-90 PET and Bremsstrahlung SPECT imaging. Dose distributions were calculated through the convolution of activity distributions with dose-voxel kernels generated using the GATE Monte Carlo toolkit. Contours were computed to represent normal tissue and target volumes. Dose-volume histograms, dose metrics, and dose profiles were compared to a ground truth dose distribution computed with GATE. The mean dose to the target for all studied voxel sizes was found to be within 5.7% of the ground truth mean dose. was shown to be strongly correlated with image voxel size of the dose distribution (r 2 = 0.90). is cited in the literature as an important dose metric and its dependence on voxel size suggests higher resolution dose distributions may provide new perspectives on dose-response relationships in yttrium-90 radioembolization. This study demonstrates that dose distributions with large voxels incorrectly homogenize the dose by attributing escalated doses to normal tissues and reduced doses in high-dose target regions. High-resolution micro-CT imaging of radiopaque microsphere distributions can provide increased confidence in characterizing the absorbed dose heterogeneity in yttrium-90 radioembolization.

Clinical Evaluation of a Three-Dimensional Internal Dosimetry Technique for Liver Radioembolization with 90Y Microspheres Using Dose Voxel Kernels.

Background: The purpose of this study was to develop a rapid, reliable, and efficient tool for three-dimensional (3D) dosimetry treatment planning and post-treatment evaluation of liver radioembolization with 90Y microspheres, using tissue-specific dose voxel kernels (DVKs) that can be used in everyday clinical practice. Materials and Methods: Two tissue-specific DVKs for 90Y were calculated through Monte Carlo (MC) simulations. DVKs for the liver and lungs were generated, and the dose distribution was compared with direct MC simulations. A method was developed to produce a 3D dose map by convolving the calculated DVKs with the activity biodistribution derived from clinical single-photon emission computed tomography (SPECT) or positron emission tomography (PET) images. Image registration for the SPECT or PET images with the corresponding computed tomography scans was performed before dosimetry calculation. The authors first compared the DVK convolution dosimetry with a direct full MC simulation on an XCAT anthropomorphic phantom. They then tested it in 25 individual clinical cases of patients who underwent 90Y therapy. All MC simulations were carried out using the GATE MC toolkit. Results: Comparison of the measured absorbed dose using tissue-specific DVKs and direct MC simulation on 25 patients revealed a mean difference of 1.07% ± 1.43% for the liver and 1.03% ± 1.21% for the tumor tissue, respectively. The largest difference between DVK convolution and full MC dosimetry was observed for the lung tissue (10.16% ± 1.20%). The DVK statistical uncertainty was <0.75% for both media. Conclusions: This semiautomatic algorithm is capable of performing rapid, accurate, and efficient 3D dosimetry. The proposed method considers tissue and activity heterogeneity using tissue-specific DVKs. Furthermore, this method provides results in <1 min, making it suitable for everyday clinical practice.

Clinical implementation of PLANET\xae\xa0Dose for dosimetric assessment after [177Lu]Lu-DOTA-TATE: comparison with Dosimetry Toolkit\xae and OLINDA/EXM\xae V1.0

BackgroundThe aim of this study was to compare a commercial dosimetry workstation (PLANET® Dose) and the dosimetry approach (GE Dosimetry Toolkit® and OLINDA/EXM® V1.0) currently used in our department for quantification of the absorbed dose (AD) to organs at risk after peptide receptor radionuclide therapy with [177Lu]Lu-DOTA-TATE.MethodsAn evaluation on phantom was performed to determine the SPECT calibration factor variations over time and to compare the Time Integrated Activity Coefficients (TIACs) obtained with the two approaches. Then, dosimetry was carried out with the two tools in 21 patients with neuroendocrine tumours after the first and second injection of 7.2 ± 0.2 GBq of [177Lu]Lu-DOTA-TATE (40 dosimetry analyses with each software). SPECT/CT images were acquired at 4 h, 24 h, 72 h and 192 h post-injection and were reconstructed using the Xeleris software (General Electric). The liver, spleen and kidneys masses and TIACs were determined using Dosimetry Toolkit® (DTK) and PLANET® Dose. The ADs were calculated using OLINDA/EXM® V1.0 and the Local Deposition Method (LDM) or Dose voxel-Kernel convolution (DK) on PLANET® Dose.ResultsWith the phantom, the 3D calibration factors showed a slight variation (0.8% and 3.3%) over time, and TIACs of 225.19 h and 217.52 h were obtained with DTK and PLANET® Dose, respectively. In patients, the root mean square deviation value was 8.9% for the organ masses, 8.1% for the TIACs, and 9.1% and 7.8% for the ADs calculated with LDM and DK, respectively. The Lin’s concordance correlation coefficient was 0.99 and the Bland–Altman plot analysis estimated that the AD value difference between methods ranged from − 0.75 to 0.49 Gy, from − 0.20 to 0.64 Gy, and from − 0.43 to 1.03 Gy for 95% of the 40 liver, kidneys and spleen dosimetry analyses. The dosimetry method had a minor influence on AD differences compared with the image registration and organ segmentation steps.ConclusionsThe ADs to organs at risk obtained with the new workstation PLANET® Dose are concordant with those calculated with the currently used software and in agreement with the literature. These results validate the use of PLANET® Dose in clinical routine for patient dosimetry after targeted radiotherapy with [177Lu]Lu-DOTA-TATE.

Dose voxel kernel prediction with neural networks for radiation dose estimation

BackgroundCurrently there is an ever increasing interest in Lu-177 targeted radionuclide therapies, which target neuro-endocrine and prostate tumours. For a patient-specific treatment, an individual dosimetry based on SPECT/CT imaging is necessary. The aim of this study is to introduce a dosimetry method, where dose voxel kernels (DVK) are predicted by a neural network. MethodsKidneys are considered one of the most critical organs in any radionuclide therapy. Hence we chose kidneys of 26 patients, who underwent Lu-177-DOTATOC or PSMA therapy, as target organs for our dosimetric method. First of all, density kernels with a size of 9×9×9 voxels were considered, and the corresponding DVKs were calculated by Monte Carlo simulations. These kernels were used to train a neural network (NN), which received a density kernel as input and predicted a DVK at the output. To predict the dose distribution of an entire kidney, the organ had to be partitioned into a large number of density kernels. Afterwards the DVKs were predicted by a trained NN, and employed to reconstruct the whole-organ dose distribution by convolution with the activity distribution. This method was compared to the standard method where the activity distribution is convolved with a DVK based on a homogeneous soft tissue kernel. ResultsThe number of training kernels amounted to 52,274 density kernels with corresponding MC-derived DVKs. The results serve as proof of principle of the newly proposed method and showed that the NN approach yielded superior results compared to the standard method with no additional computational effort. ConclusionThe NN approach is an accurate and highly competitive dosimetric method to precisely estimate absorbed radiation dose in critical organs like kidneys in clinical routine. To further improve the results, a larger number of DVKs needs to be computed by Monte Carlo simulations. An extension of the method to other organs is easily conceivable.

A dose voxel kernel method for rapid reconstruction of out-of-field neutron dose of patients in pencil beam scanning (PBS) proton therapy

Monte Carlo (MC) radiation transport methods are used for dose calculation as ‘gold standard.’ However, the method is computationally time-consuming and thus impractical for normal tissue dose reconstructions for the large number of proton therapy patients required for epidemiologic investigations of late health effects. In the present study, we developed a new dose calculation method for the rapid reconstruction of out-of-field neutron dose to patients undergoing pencil beam scanning (PBS) proton therapy. The new dose calculation method is based on neutron dose voxel kernels (DVKs) generated by MC simulations of a proton pencil beam irradiating a water phantom (60 × 60 × 300 cm3), which was conducted using a MC proton therapy simulation code, TOPAS. The DVKs were generated for 19 beam energies (from 70 to 250 MeV with the 10 MeV interval) and three range shifter thicknesses (1, 3, and 5 cm). An in-house program was written in C++ to superimpose the DVKs onto a patient CT images according to proton beam characteristics (energy, position, and direction) available in treatment plans. The DVK dose calculation method was tested by calculating organ/tissue-specific neutron doses of 1- and 5-year-old whole-body computational phantoms where intracranial and craniospinal irradiations were simulated. The DVK-based doses generally showed reasonable agreement with those calculated by direct MC simulations with a detailed PBS model that were previously published, with differences mostly less than 30% and 10% for the intracranial and craniospinal irradiations, respectively. The computation time of the DVK method for one patient ranged from 1 to 30 min on a single CPU core of a personal computer, demonstrating significant improvement over the direct MC dose calculation requiring several days on high-performance computing servers. Our DVK-based dose calculation method will be useful when dosimetry is needed for the large number of patients such as for epidemiologic or clinical research.

A comparison of methods for adapting dose-voxel-kernels to tissue inhomogeneities

In radionuclide therapies, dosimetry is used for determining patient-individual dose burden. Standard approaches provide whole organ doses only. For assessing dose heterogeneity inside organs, voxel-wise dosimetry based on 3D SPECT/CT imaging could be applied. Often, this is achieved by convolving voxel-wise time-activity-curves with appropriate dose-voxel-kernels (DVK). The DVKs are meant to model dose deposition, and can be more accurate if modelled for the specific tissue type under consideration. In literature, DVKs are often not adapted to these inhomogeneities, or simple approximation schemes are applied.For 26 patients, which had previously undergone a -PSMA or -DOTATOC therapy, decay maps, mass-density maps as well as tissue-type maps were derived from SPECT/CT acquisitions. These were used for a voxel-based dosimetry based on convolution with DVKs (each of size ) obtained by four different DVK methods proposed in literature. The simplest only considers a spatially constant soft-tissue DVK (herein named ‘constant’), while others either take into account only the local density of the center voxel of the DVK (herein named ‘center-voxel’) or scale each voxel linearly according to the proper mass density deduced from the CT image (herein named ‘density’) or considered both the local mass density as well as the direct path between the center voxel and any voxel in its surrounding (herein named ‘percentage’). Deviations between resulting dose values and those from full Monte-Carlo simulations (MC simulations) were compared for selected organs and tissue-types.For each DVK method, inter-patient variability was considerable showing both under- and over-estimation of energy dose compared to the MC result for all tissue densities higher than soft tissue. In kidneys and spleen, ‘constant’ and ‘density’-scaled DVKs achieved estimated doses with smallest deviations to the full MC gold standard (∼ underestimation). For low and high density tissue types such as lung and adipose or bone tissue, alternative DVK methods like ‘center-voxel’- and ‘percentage’- scaled achieved superior results, respectively. Concerning computational load, dose estimation with the DVK method ‘constant’ needs about 1.1 s per patient, center-voxel scaling amounts to 1.2 s, density scaling needs 1.4 s while percentage scaling consumes 860.3 s per patient.In this study encompassing a large patient cohort and four different DVK estimation methods, no single DVK-adaption method was consistently better than any other in case of soft tissue kernels. Hence in such cases the simplest DVK method, labeled ‘constant’, suffices. In case of tumors, often located in tissues of low (lung) or high (bone) density, more sophisticated DVK methods excel. The high inter-patient variability indicates that for evaluating new algorithms, a sufficiently large patient cohort needs to be involved.

36. Selective internal radiation therapy with yttrium-90: Optimization of pre-therapeutic dosimetry

Introduction During the simulation step of a selective internal radiation therapy (SIRT) with yttrium-90, SPECT/CT images using 99mTc-MAA (macroaggregated human albumin) are acquired in order to assess the lung shunt and the MAA biodistribution in the healthy liver and the tumour. A predictive dosimetry is then usually performed in order to estimate the mean absorbed dose delivered to the lungs, the healthy liver and the tumour during the treatment. The aim of this study was to find a simple and quantitative segmentation method that can easily be transferred to clinical routine. We chose to use 99mTc-MAA SPECT images alone and we compared this method with two other historically methods used in this procedure: anatomical segmentation obtained from CT alone and from co-registered SPECT/CT images. Methods The dosimetric reconstruction tool currently in development uses the Geant4 and the GATE Monte Carlo codes to provide dose maps in the area of an irradiation accident. An important feature of the simulation Two calibration curves were obtained from phantom studies using a NEMA/IEC phantom. The first curve was used to determine which threshold (SPECTthres) should be applied to match the actual organs volumes according to measured the signal to noise ratio (SNR) in the images. The second curve enabled the user to get quantitative data from the chosen threshold. This method was validated using the Stratos software with an anthropomorphic torso phantom: the mean absorbed doses to the tumour and the healthy liver were estimated by Voxel Dose Kernel (VDK) dosimetry and compared to the theoretical mean absorbed doses calculated with the MIRD formalism. The method was then compared with two other segmentation techniques and retrospectively applied to 5 patients. Results In phantom studies, the image derived characteristics of the healthy liver were preserved (volume deviation Conclusion Besides its ease of implementation in clinical routine, the SPECTthres technique allows operators to estimate accurately the volume and activity of 3D delineated object in the framework of SIRT with yttrium-90.

Evaluating the Application of Tissue-Specific Dose Kernels Instead of Water Dose Kernels in Internal Dosimetry: A Monte Carlo Study.

The aim of this work is to evaluate the application of tissue-specific dose kernels instead of water dose kernels to improve the accuracy of patient-specific dosimetry by taking tissue heterogeneities into consideration. Tissue-specific dose point kernels (DPKs) and dose voxel kernels (DVKs) for yttrium-90 (90Y), lutetium-177 (177Lu), and phosphorus-32 (32P) are calculated using the Monte Carlo (MC) simulation code GATE (version 7). The calculated DPKs for bone, lung, adipose, breast, heart, intestine, kidney, liver, and spleen are compared with those of water. The dose distribution in normal and tumorous tissues in lung, liver, and bone of a Zubal phantom is calculated using tissue-specific DVKs instead of those of water in conventional methods. For a tumor defined in a heterogeneous region in the Zubal phantom, the absorbed dose is calculated using a proposed algorithm, taking tissue heterogeneity into account. The algorithm is validated against full MC simulations. The simulation results indicate that the highest differences between water and other tissue DPKs occur in bone for 90Y (12.2% ± 0.6%), 32P (18.8% ± 1.3%), and 177Lu (16.9% ± 1.3%). The second highest discrepancy corresponds to the lung for 90Y (6.3% ± 0.2%), 32P (8.9% ± 0.4%), and 177Lu (7.7% ± 0.3%). For 90Y, the mean absorbed dose in tumorous and normal tissues is calculated using tissue-specific DVKs in lung, liver, and bone. The results are compared with doses calculated considering the Zubal phantom water equivalent and the relative differences are 4.50%, 0.73%, and 12.23%, respectively. For the tumor in the heterogeneous region of the Zubal phantom that includes lung, liver, and bone, the relative difference between mean calculated dose in tumorous and normal tissues based on the proposed algorithm and the values obtained from full MC dosimetry is 5.18%. A novel technique is proposed considering tissue-specific dose kernels in the dose calculation algorithm. This algorithm potentially enables patient-specific dosimetry and improves estimation of the average absorbed dose of 90Y in a tumor located in lung, bone, and soft tissue interface by 6.98% compared with the conventional methods.

SU-F-BRA-15: Physical Aspects and Clinical Applications of Post-Treatment Yttrium-90 PET-Based Dosimetry

Purpose: The accuracy of PET-based post-treatment dosimetry of yttrium-90 microspheres has been improving over the past decade and is now at a stage, permitting volumetric dose-outcome studies. We outline the recent advances and identify the physical limitations to the accuracy of the dose calculations. Methods: Convolution of the measured PET activity density distribution with a pre-calculated voxel-dose-kernel (VDK) is the most widely used method for dose reconstruction. Therefore, accurate knowledge of the beta+ branching ratio as well as the micro-dosimetric characteristics of electron interactions within the microsphere is essential for the computation of the dose kernel. We implement a model of the microspheres used in our clinic to calculate the modified electron energy spectrum at the microsphere’s surface and to determine the impact of self-shielding on the dose reconstruction. The three-dimensional dose distributions obtained for 10 patients treated with radio-embolization with yttrium-90 microspheres are evaluated and various DVH markers are investigated for correlation with outcome. Results: The methods for reducing the overall computation uncertainty are systematically outlined in this presentation. Since the latest experimental data on yttrium-90 beta+ branching ratio has a relative uncertainty of 1.5%, all contributing factors derived from Monte Carlo simulations must be brought to a sub 1% level. The self-shielding within the microspheres is found to be responsible for up to 6% reduction of the reconstructed dose in low-gradient regions and must be taken into account. The contribution of trace amounts of other beta+ emitters introduced during the manufacturing process is also discussed. Conclusion: The accelerating pace of clinical adoption of PET-based post-treatment dosimetry is mainly due to advances in both quantitative PET imaging and physical models of dose deposition. We show that the overall physical dose uncertainty in the convolution step can be further reduced, thus making ongoing multi-institutional dose-outcome studies even more reliable.

Patient dosimetry for selective internal radiation treatment based on PET imaging

Until recently, the radiation dose to patients undergoing the 90Y selective internal radiation treatment (SIRT) procedure is determined by applying the partition model to 99mTc MAA pretreatment scan. There can be great uncertainty in radiation dose calculated from this approach and we presented a method to compute the 3D dose distributions resulting from 90Y SIRT based on 90Y positron emission tomography (PET) imaging. Five 90Y SIRT treatments were retrospectively analyzed. After 90Y SIRT, patients had 90Y PET/CT imaging within 6 hours of the procedure. To obtain the 3D dose distribution of the patients, their respective 90Y PET images were convolved with a Monte Carlo generated voxel dose kernel. The sensitivity of the PET/CT scanner for 90Y was determined through phantom studies. The 3D dose distributions were then presented in DICOM RT dose format. By applying the linear quadratic model to the dose data, we derived the biologically effective dose and dose equivalent to 2 Gy/fraction delivery, taking into account the spatial and temporal dose rate variations specific for SIRT. Based on this data, we intend to infer tumor control probability and risk of radiation induced liver injury from SIRT by comparison with established dose limits. For the five cases, the mean dose to target ranged from 51.7 ± 28.6Gy to 163 ± 53.7 Gy. Due to the inhomogeneous nature of the dose distribution, the GTVs were not covered adequately, leading to very low values of tumor control probability. The mean dose to the normal liver ranged from 21.4 ± 30.7 to 36.7 ± 25.9 Gy. According to QUANTEC recommendation, a patient with primary liver cancer and a patient with metastatic liver cancer has more than 5% risk of radiotherapy‐induced liver disease (RILD).PACS number: 87.53.Bn

3D inpatient dose reconstruction from the PET-CT imaging of 90 Y microspheres for metastatic cancer to the liver: Feasibility study

The introduction of radioembolization with microspheres represents a significant step forward in the treatment of patients with metastatic disease to the liver. This technique uses semiempirical formulae based on body surface area or liver and target volumes to calculate the required total activity for a given patient. However, this treatment modality lacks extremely important information, which is the three-dimensional (3D) dose delivered by microspheres to different organs after their administration. The absence of this information dramatically limits the clinical efficacy of this modality, specifically the predictive power of the treatment. Therefore, the aim of this study is to develop a 3D dose calculation technique that is based on the PET imaging of the infused microspheres. The Fluka Monte Carlo code was used to calculate the voxel dose kernel for 90Y source with voxel size equal to that of the PET scan. The measured PET activity distribution was converted to total activity distribution for the subsequent convolution with the voxel dose kernel to obtain the 3D dose distribution. In addition, dose-volume histograms were generated to analyze the dose to the tumor and critical structures. The 3D inpatient dose distribution can be reconstructed from the PET data of a patient scanned after the infusion of microspheres. A total of seven patients have been analyzed so far using the proposed reconstruction method. Four patients underwent treatment with SIR-Spheres for liver metastases from colorectal cancer and three patients were treated with Therasphere for hepatocellular cancer. A total of 14 target tumors were contoured on post-treatment PET-CT scans for dosimetric evaluation. Mean prescription activity was 1.7 GBq (range: 0.58-3.8 GBq). The resulting mean maximum measured dose to targets was 167 Gy (range: 71-311 Gy). Mean minimum dose to 70% of target (D70) was 68 Gy (range: 25-155 Gy). Mean minimum dose to 90% of target (D90) was 53 Gy (range: 13-125 Gy). A three-dimensional inpatient dose reconstruction method has been developed that is based on the PET/CT data of a patient treated with 90Y microspheres. It allows for a complete description of the absorbed dose by the tumor and critical structures. It represents the first step in building predictive models for treatment outcomes for patients receiving this therapeutic modality as well as it allows for better analysis of patients' dose response and will ultimately improve future treatment administration.

WE‐A‐BRB‐06: 3D In‐Patient Dose Reconstruction from the PET‐CT Imaging of Y‐90 Microspheres for Metastatic Cancer to the Liver

Purpose: The development of radioembolization with SIR‐Spheres represents a significant advance in the treatment of patients with metastatic disease to the liver. This technique has evolved to use a formula for dose calculation that relies on body surface area as the main determinant of dose. However, what is prescribed is not dose, but rather activity. It has been traditionally thought that the tracer 90Y is a pure electron emitter. However, the decay of 90Y has also a minor ß+ branch to the first excited state of 90Zr, with 34 ppm branching ratio. While the positron emission is rare, it can be observed in a PET scan. Therefore the main objective of this work is to develop a new method for 3D dose reconstruction based on the PET imaging of a patient injected with SIR‐Spheres. Methods: Using Fluka Monte Carlo code, the voxel dose kernel (VDK) was calculated for 90Y source of size equal to that of the PET scanner voxel size. Subsequently, the convolution of the VDK with measured PET data was performed to recover the absorbed dose. The absolute dose calibration was done by taking the ratio of the measured positron activity to the known activity due to electrons Results: The developed model was used to reconstruct the dose distribution for two patients treated with 90Y microspheres. The 3D dose calculation was subsequently superimposed on the patient's CT information to provide a complete description of the absorbed dose to the liver, tumor and adjacent structures. Conclusions: The proposed method offers significant improvement in characterization of the dose deposited by SIR‐Spheres with the hope that this may help answer some of the clinical questions concerning the influence of the dose distribution on the response rate, progression‐free or overall survival.