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Active SBIR-STTR RPGS NIH (US)

Torch Recon: An Innovative Reconstruction Software for Increased Throughput and Improved Low-Count Quantitative SPECT Imaging

$10.04M USD

Funder NATIONAL CANCER INSTITUTE
Recipient Organization Voximetry, Inc.
Country United States
Start Date Sep 19, 2024
End Date Aug 31, 2026
Duration 711 days
Number of Grantees 1
Roles Principal Investigator
Data Source NIH (US)
Grant ID 10922106
Grant Description

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.

All Grantees

Voximetry, Inc.

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