PROJECT SUMMARY/ABSTRACT Single-photon emission computed tomography (SPECT) imaging plays a pivotal role in radiopharmaceutical therapy (RPT), allowing clinicians to personalize prescriptions and assess treatment response. However, traditional SPECT reconstruction methods often encounter challenges related to noise, artifacts, and lengthy processing times. In imaging, accurate correction of scattering effects and enhancement of signal-to-noise ratio (SNR) are critical for achieving quantitively accurate images required for dosimetry guidance of RPT and treatment response assessment. Torch Recon is a cutting-edge software that harnesses the synergistic power of Monte Carlo simulation and deep learning techniques to address the limitations of conventional reconstruction methods. Monte Carlo simulation accurately models photon interactions within tissues, leading to improved accuracy and resolution in the reconstructed images. Complementing this, deep learning algorithms are employed to enhance image quality, reduce noise, and suppress artifacts. These algorithms leverage large datasets to learn intricate patterns and relationships, resulting in sharper, more informative SPECT images. Torch Recon represents the fusion of Monte Carlo simulation and deep learning, enabling a dynamic and adaptive reconstruction process which has the potential to not only improve quantitative SPECT but also SNR which is especially important for scenarios with low counting statistics, e.g., alpha emitters. As part of a previous Phase I contract, we incorporated a SPECT reconstruction algorithm with a GPU-accelerated Monte Carlo-based scatter estimator into the GPU-based Torch software system for RPT dosimetry. In this Phase II proposal, we will (1) implement AI denoising techniques into Torch Recon, (2) assess accuracy and performance of the reconstruction software using phantoms, and (3) validate clinical usability and effectiveness through a prospective clinical trial. By completing the milestones of this Phase II proposal, Torch Recon will be ready for 510(k) clearance and commercialization.