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Background: Radiopharmaceutical therapy (RPT) is an increasingly adopted modality for treating cancer. There is evidence that the optimization of the treatment based on dosimetry can improve outcomes. However, standardization of the clinical dosimetry workflow still represents a major effort. Among the many sources of variability, the impact of using different Dose Voxel Kernels (DVKs) to generate absorbed dose (AD) maps by convolution with the time‐integrated activity (TIA) distribution has not been systematically investigated. Purpose: This study aims to compare DVKs and assess the differences in the ADs when convolving the same TIA map with different DVKs. Methods: DVKs of 3 × 3 × 3 mm3 sampling—nine for 177Lu, nine for 90Y—were selected from those most used in commercial/free software or presented in prior publications. For each voxel within a 11 × 11 × 11 matrix, the coefficient of variation (CoV) and the percentage difference between maximum and minimum values (% maximum difference) were calculated. The total absorbed dose per decay (SUM), calculated as the sum of all the voxel values in each kernel, was also compared. Publicly available quantitative SPECT images for two patients treated with 177Lu‐DOTATATE and PET images for two patients treated with 90Y‐microspheres were used, including organs at risk (177Lu: kidneys; 90Y: liver and healthy liver) and tumors' segmentations. For each patient, the mean AD to the volumes of interest (VOIs) was calculated using the different DVKs, the same TIA map and the same software tool for dose convolution, thereby focusing on the DVK impact. For each VOI, the % maximum difference of the mean AD between maximum and minimum values was computed. Results: The CoV (% maximum difference) in voxels of normalized coordinates [0,0,0], [0,1,0], and [0,1,1] were 5%(21%), 9%(35%), and 10%(46%) for the 177Lu DVKs. For the case of 90Y, these values were 2%(9%), 4%(14%), and 4%(16%). The CoV (% maximum difference) for SUM was 9%(33%) for 177Lu, and 4%(15%) for 90Y. The variability of the mean tumor and organ AD was up to 19% and 15% in 177Lu‐DOTATATE and 90Y‐microspheres patients, respectively. Conclusions: This study showed a considerable AD variability due exclusively to the use of different DVKs. A concerted effort by the scientific community would contribute to decrease these discrepancies, strengthening the consistency of AD calculation in RPT. [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]

Purpose: 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. Methods: 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 (3.3 cm) 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. Results: 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×$\; \times \;$512×$\; \times \;$130 dose‐rate map for a patient. Conclusion: 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. [ABSTRACT FROM AUTHOR]

Patient-specific dosimetry in radiopharmaceutical therapy (RPT) is impeded by the lack of tools that are accurate and practical for the clinic. Our aims were to construct and test an integrated voxel-level pipeline that automates key components (organ segmentation, registration, dose-rate estimation, and curve fitting) of the RPT dosimetry process and then to use it to report patient-specific dosimetry in 177 Lu-DOTATATE therapy. Methods: An integrated workflow that automates the entire dosimetry process, except tumor segmentation, was constructed. First, convolutional neural networks (CNNs) are used to automatically segment organs on the CT portion of one post-therapy SPECT/CT scan. Second, local contour intensity-based SPECT--SPECT alignment results in volume-of-interest propagation to other time points. Third, dose rate is estimated by explicit Monte Carlo (MC) radiation transport using the fast, Dose Planning Method code. Fourth, the optimal function for dose-rate fitting is automatically selected for each voxel. When reporting mean dose, we apply partial-volume correction, and uncertainty is estimated by an empiric approach of perturbing segmentations. Results: The workflow was used with 4-time-point 177 Lu SPECT/CT imaging data from 20 patients with 77 neuroendocrine tumors, segmented by a radiologist. CNN-defined kidneys resulted in high Dice values (0.91-0.94) and only small differences (2%-5%) in mean dose when compared with manual segmentation. Contour intensity-based registration led to visually enhanced alignment, and the voxel-level fitting had high R 2 values. Across patients, dosimetry results were highly variable; for example, the average of the mean absorbed dose (Gy/GBq) was 3.2 (range, 0.2-10.4) for lesions, 0.49 (range, 0.24-1.02) for left kidney, 0.54 (range, 0.31-1.07) for right kidney, and 0.51 (range, 0.27-1.04) for healthy liver. Patient results further demonstrated the high variability in the number of cycles needed to deliver hypothetical threshold absorbed doses of 23 Gy to kidney and 100 Gy to tumor. The uncertainty in mean dose, attributable to variability in segmentation, averaged 6% (range, 3%-17%) for organs and 10% (range, 3%-37%) for lesions. For a typical patient, the time for the entire process was about 25 min (∼2 min manual time) on a desktop computer, including time for CNN organ segmentation, coregistration, MC dosimetry, and voxel curve fitting. Conclusion: A pipeline integrating novel tools that are fast and automated provides the capacity for clinical translation of dosimetry-guided RPT., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)