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

Award Number: R44CA291564
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/19/2024
PERIOD OF PERFORMANCE END DATE: 08/31/2026

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $0 )
2026	2025	VOXIMETRY INC	8517 EXCELSIOR DR STE 207	MADISON	WI	53717	DANE	USA	Cancer Detection and Diagnosis Research	000	2	3/17/2026	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $985,549 )
2025	2025	VOXIMETRY INC	8517 EXCELSIOR DR STE 207	MADISON	WI	53717	DANE	USA	Cancer Detection and Diagnosis Research	001	2	7/30/2025	NON-COMPETING CONTINUATION	$985,549
2025	2024	VOXIMETRY INC	8517 EXCELSIOR DR STE 207	MADISON	WI	53717	DANE	USA	Cancer Detection and Diagnosis Research	000	1	12/23/2024	NEW	$0
														Subtotal = $985,549

Issue Date FY: 2024 ( Subtotal = $1,003,632 )
2024	2024	VOXIMETRY INC	8517 EXCELSIOR DR STE 207	MADISON	WI	53717	DANE	USA	Cancer Detection and Diagnosis Research	000	1	9/19/2024	NEW	$1,003,632
														Subtotal = $1,003,632

Grand Total All Awards = $1,989,181

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

Award Number: R44CA291564

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/19/2024

PERIOD OF PERFORMANCE END DATE: 08/31/2026

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