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α-particle emitters are emerging as a potent modality for disseminated cancer therapy because of their high linear energy transfer and localized absorbed dose profile. Despite great interest and pharmaceutical development, there is scant information on the distribution of these agents at the scale of the α-particle pathlength. We sought to determine the distribution of clinically approved [ 223 Ra]RaCl 2 in bone metastatic castration-resistant prostate cancer at this resolution, for the first time to our knowledge, to inform activity distribution and dose at the near-cell scale. Methods: Biopsy specimens and blood were collected from 7 patients 24 h after administration. 223 Ra activity in each sample was recorded, and the microstructure of biopsy specimens was analyzed by micro-CT. Quantitative autoradiography and histopathology were segmented and registered with an automated procedure. Activity distributions by tissue compartment and dosimetry calculations based on the MIRD formalism were performed. Results: We revealed the activity distribution differences across and within patient samples at the macro- and microscopic scales. Microdistribution analysis confirmed localized high-activity regions in a background of low-activity tissue. We evaluated heterogeneous α-particle emission distribution concentrated at bone-tissue interfaces and calculated spatially nonuniform absorbed-dose profiles. Conclusion: Primary patient data of radiopharmaceutical therapy distribution at the small scale revealed that 223 Ra uptake is nonuniform. Dose estimates present both opportunities and challenges to enhance patient outcomes and are a first step toward personalized treatment approaches and improved understanding of α-particle radiopharmaceutical therapies., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Digital autoradiography (DAR) is a powerful tool to quantitatively determine the distribution of a radiopharmaceutical within a tissue section and is widely used in drug discovery and development. However, the low image resolution and significant background noise can result in poor correlation, even errors, between radiotracer distribution, anatomic structure, and molecular expression profiles. Differing from conventional optical systems, the point-spread function in DAR is determined by properties of radioisotope decay, phosphor, and digitizer. Calibration of an experimental point-spread function a priori is difficult, prone to error, and impractical. We have developed a content-adaptive restoration algorithm to address these problems. Methods: We model the DAR imaging process using a mixed Poisson-gaussian model and blindly restore the image by a penalized maximum-likelihood expectation-maximization algorithm (PG-PEM). PG-PEM implements a patch-based estimation algorithm with density-based spatial clustering of applications with noise to estimate noise parameters and uses L2 and Hessian Frebonius norms as regularization functions to improve performance. Results: First, PG-PEM outperformed other restoration algorithms at the denoising task ( P < 0.01). Next, we implemented PG-PEM on preclinical DAR images ( 18 F-FDG, treated mouse tumor and heart; 18 F-NaF, treated mouse femur) and clinical DAR images (bone biopsy sections from 223 RaCl 2 -treated castration-resistant prostate cancer patients). DAR images restored by PG-PEM of all samples achieved a significantly higher effective resolution and contrast-to-noise ratio and a lower SD of background ( P < 0.0001). Additionally, by comparing the registration results between the clinical DAR images and the segmented bone masks from the corresponding histologic images, we found that the radiopharmaceutical distribution was significantly improved ( P < 0.0001). Conclusion: PG-PEM is able to increase resolution and contrast while robustly accounting for DAR noise and demonstrates the capacity to be widely implemented to improve preclinical and clinical DAR imaging of radiopharmaceutical distribution., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)