Direct estimation of activity concentration in regional voxels with application to ¹⁷⁷ Lu peptide receptor radionuclide therapy

BACKGROUND: Quantitative SPECT in radionuclide-therapy is limited by partial-volume effects (PVEs). The implementation of regional voxels (r.v.), estimating mean activity concentrations in regions directly from projections, offers a promising alternative for the geometry-specification to reduce PVEs. PURPOSE: Lu-PRRT). METHODS: Lu]Lu-DOTA-TATE with SPECT acquired 1 d p.i. (dataset PB). Activity concentration was estimated from reconstruction with cu.v. and using r.v. for both datasets, with multiple noise realizations for PA using bootstrapping. Organ delineation was performed based on CT using the AI tool TotalSegmentator, and tumor delineation made in cu.v. SPECT images. The estimated activity concentration for kidneys, spleen, and tumors from r.v. was compared to that obtained with cu.v. with and without post-reconstruction PVC. To study the accuracy of activity-concentration estimates, simulations were performed with the SIMIND Monte Carlo program with patient images used as basis. The sensitivity to misalignments between SPECT and CT was also evaluated. RESULTS: For both patient and simulated data, activity concentrations estimated with r.v. are higher than those from cu.v., with comparable standard deviations. Mean relative errors for simulated images from PA relative to simulation input at 1 d p.i. reconstructions with r.v. are (-4.6 ± 1.4) %, (3.0 ± 0.5) %, (0.1 ± 0.5) %, and (5.6 ± 1.2) % for tumor, left kidney, right kidney, and spleen, respectively. Corresponding results for cu.v. with post-reconstruction PVC are (-12.3 ± 2.2) %, (-4.2 ± 0.6) %, (-7.0 ± 0.5) %, and (-2.1 ± 1.1) %. For simulated images based on PB, the mean relative errors obtained for r.v. are (-3.1 ± 3.5) %, (1.2 ± 1.2) %, (-1.7 ± 1.1) %, and (2.3 ± 0.8) %, while for cu.v. with PVC they are (-7.9 ± 6.7) %, (-5.8 ± 1.9) %, (-9.0 ± 1.0) %, and (-0.7 ± 2.6) %. CONCLUSIONS: Lu-PRRT and demonstrates lower sensitivity to misregistration errors. For tumors, r.v. yields lower systematic errors than cu.v. but demonstrates a higher sensitivity to image segmentation errors for volumes below approximately 10 mL.