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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.