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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]

A fast algorithm is presented for back-projection of Compton camera projection data in a list mode fashion, in which the number of operations per photon per image slice scales at less than the dimension N of the reconstructed image. The method focuses on the mesh of grid lines which delineate the pixels in the image, rather than on the pixels themselves, and involves computation of the intersection of back-projected Compton cones with just the outermost edges in the image and those grid lines near these image boundary intersection points, for each plane (or slice) of the image. Depending on the orientation of the back projected cone and the image space grid lines, between 4 and N + 4 determinations (by quadratic solution) of cone/mesh intersections are required per image slice per photon. Thus the total number of floating point operations per image slice scales as 4 to N+4 times the number of detected photons. Three dimensional back-projections can be obtained simply by stepping through the sequence of planes in the image space parallel to the Compton aperture. Back-projected images are presented, using data generated by Monte Carlo simulations of a practical detector configuration and various source distributions.

A technique has been devised for independently determining the energy and spatial resolution components of the point-spread functions of electronically collimated (Compton scatter) imaging devices using a Monte Carlo method. Monte Carlo runs are performed for the device being examined, and detailed records of exact energy loss and spatial coordinates of each interaction for each particle in the simulation are made. Taking resolution parameters from the known performance of the detectors, measured data is simulated by sampling from Gaussian distributions (for both energy and position) about the exact interaction energy and position in both detectors, for each Monte Carlo history. By back-projecting cones on an event-by-event basis and calculating the closest approach of the cone to the known source point using the simulated measurement data for one of the energy and position variables and the exact interaction data for the rest of the variables, the effect of the variable in question on the point spread can be isolated. In this paper we present the calculations of the contribution to the point-spread function of the energy and spatial resolution in each detector for various source positions for a representative Compton camera configuration. Index Terms - Compton scatter camera, Monte Carlo, resolution.

Digital x-ray imaging detectors designed for soft x-rays (1 to 50 keV) are significant for medical mammography, dental radiography, microradiography, and microtomography. Detector designs involve either direct absorption of x-rays in solid state devices or thin scintillator screens optically coupled to solid state sensors. Well designed scintillator systems produce 10 or more electrons per detected x-ray and, used with charge coupled devices (CCD), detect 100,000 x-rays per pixel before saturation. However, if the scintillator is directly coupled to the detector, radiation can penetrate to the semiconductor detector with a small number of events producing large charge and noise. We have investigated the degradation of image noise by these direct absorption events using numerical models for a laboratory detector system consisting of a 60 [[micro]meter] CsI scintillator optically coupled to a scientific CCD. Monte Carlo methods were used to estimate the charge deposition signal and noise for both the CsI and the semiconductor. Without a fiber optic coupler, direct absorptions dominate the signal and increase the signal variance by a factor of about 30 at energies above 10 keV. With a 3 mm fiber optic coupler, no significant degradation is observed for input energies below 45 keV.

In this paper we propose a 3D image reconstruction algorithm for a 3D Compton camera being developed at the University of Michigan. We present a mathematical model of the transition matrix of the camera which exploits symmetries by using an adapted spatial sampling pattern in the object domain. For each projection angle, the sampling pattern is uniform over a set of equispaced nested hemispheres. By using this sampling pattern the system matrix is reduced to a product of a (approximately) block circulant matrix and a sparse interpolation matrix. This representation reduces the very high storage and computation requirement inherent to 3D reconstruction using transition matrix inversion methods. We geometrically optimize our hemispherical sampling and propose a 3D volumetric interpolation. Finally, we present a 3D image reconstruction method which uses the Gauss-Seidel algorithm to minimize a penalized least square objective.

Purpose: Improved data collection methods have improved absorbed dose estimation by tracking activity distributions and tumor extent at multiple time points, allowing individualized absorbed dose estimation. Treatment with tositumomab and I-tositumomab anti-CD20 radioimmunotherapy (BEXXAR) yields a cold antibody antitumor response (cold protein effect) and a radiation response. Biologically effective contributions, including the cold protein effect, are included in an equivalent biological effect model that was fit to patient data. Methods: Fifty-seven tumors in 19 patients were followed using 6 single proton emission computed tomography (SPECT)/CT studies, 3 each post tracer (5 mCi) and therapy (∼100 mCi) injections with tositumomab and I-tositumomab. Both injections used identical antibody mass, a flood dose of 450 mg plus 35 mg of I tagged antibody. The SPECT/CT data were used to calculate absorbed dose rate distributions and tumor and whole-body time-activity curves, yielding a space-time dependent absorbed dose rate description for each tumor. Tumor volume outlines on CT were used to derive the time dependence of tumor size for tracer and therapy time points. A combination of an equivalent biological effect model and an inactivated cell clearance model was used to fit absorbed dose sensitivity and cold effect sensitivity parameters to tumor shrinkage data, from which equivalent therapy values were calculated. Results: Patient responses were categorized into three groups: standard radiation sensitivity with no cold effect (7 patients), standard radiation sensitivity with cold effect (11 patients), and high radiation sensitivity with cold effect (1 patient). Conclusion: Fit parameters can be used to categorize patient response, implying a potential predictive capability. [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.)

Unlabelled: (131)I radionuclide therapy studies have not shown a strong relationship between tumor absorbed dose and response, possibly due to inaccuracies in activity quantification and dose estimation. The goal of this work was to establish the accuracy of (131)I activity quantification and absorbed dose estimation when patient-specific, 3-dimensional (3D) methods are used for SPECT reconstruction and for absorbed dose calculation., Methods: Clinically realistic voxel-phantom simulations were used in the evaluation of activity quantification and dosimetry. SPECT reconstruction was performed using an ordered-subsets expectation maximization (OSEM) algorithm with compensation for scatter, attenuation, and 3D detector response. Based on the SPECT image and a patient-specific density map derived from CT, 3D dosimetry was performed using a newly implemented Monte Carlo code. Dosimetry was evaluated by comparing mean absorbed dose estimates calculated directly from the defined phantom activity map with those calculated from the SPECT image of the phantom. Finally, the 3D methods were applied to a radioimmunotherapy patient, and the mean tumor absorbed dose from the new calculation was compared with that from conventional dosimetry obtained from conjugate-view imaging., Results: Overall, the accuracy of the SPECT-based absorbed dose estimates in the phantom was >12% for targets down to 16 mL and up to 35% for the smallest 7-mL tumor. To improve accuracy in the smallest tumor, more OSEM iterations may be needed. The relative SD from multiple realizations was <3% for all targets except for the smallest tumor. For the patient, the mean tumor absorbed dose estimate from the new Monte Carlo calculation was 7% higher than that from conventional dosimetry., Conclusion: For target sizes down to 16 mL, highly accurate and precise dosimetry can be obtained with 3D methods for SPECT reconstruction and absorbed dose estimation. In the future, these methods can be applied to patients to potentially establish correlations between tumor regression and the absorbed dose statistics from 3D dosimetry.