A pipeline for automated voxel dosimetry: application in patients with multi-SPECT/CT imaging following ¹⁷⁷Lu peptide receptor radionuclide therapy

Patient-specific dosimetry in radiopharmaceutical therapy (RPT) is impeded by the lack of tools that are accurate and practical for the clinic. The 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 ¹⁷⁷Lu-DOTATATE therapy. Methods: An integrated workflow that automates the entire dosimetry process, except tumor segmentation, was constructed. 1) Convolutional neural networks (CNNs) are used to auto-segment organs on CT of SPECT/CT; 2) Local contour intensity-based SPECT-SPECT alignment results in volume-of-interest propagation to other timepoints; 3) Dose-rate estimation is performed by explicit Monte Carlo (MC) using the fast, Dose-Planning Method code; 4) The optimal function for dose-rate fitting is automatically selected for each voxel. When reporting mean dose, partial volume correction is applied, and uncertainty is estimated by an empirical approach of perturbing segmentations. Results: The workflow was used with 4-timepoint 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 R2 values. Across patients, dosimetry results were highly variable, for example, the average (range) of the mean absorbed dose in Gy/GBq were: lesions, 3.2(0.2-10.4); L kidney, 0.49(0.24-1.02); R kidney 0.54(0.31-1.07) and healthy liver, 0.51(0.27-1.04) 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, was on average (range) 6% (3-17%) for organs and 10% (3-37%) for lesions. For a typical patient, the time for the entire process was ~ 25 minutes (~ 2 min manual time) on a desktop computer, including time for CNN organ segmentation, co-registration, 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.