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Multi-modality Image Fusion to Improve Coronary Revascularization in Patients with Stable Coronary Artery Disease

Award Number: R15HL172198
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 12/20/2023
PERIOD OF PERFORMANCE END DATE: 11/30/2026

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Multi-modality Image Fusion to Improve Coronary Revascularization in Patients with Stable Coronary Artery Disease - PROJECT SUMMARY Coronary revascularization (CR) is a standard interventional treatment for patients with symptomatic stable coronary artery disease (CAD). However, the performance of such interventions without appropriate patient selection is not superior to medical therapy alone. Consequently, to achieve enhanced patient selection, many have advocated for physiological/anatomical information integration of myocardial functions and coronary arteries before the CR. As a result, we aim to develop enabling technologies allowing for comprehensive and quantitative assessments of myocardial functions and coronary arteries. Leveraging the proposed technologies, an interventional cardiologist can identify the most appropriate lesions to treat using CR, impacting the management of patients with CAD. This project first combines gated SPECT myocardial perfusion imaging (MPI) with invasive coronary angiography (ICA). Then, machine-learning-based methods can reliably interpret the fusion results to support clinical decision- making. Studies have already demonstrated that CR decisions based on MPI improve outcomes over anatomical assessment or medical therapy alone. Once ICA is combined, the SPECT-ICA fusion map provides complementary information about both myocardial functions (perfusion and wall motion) and coronary arteries (anatomy and assessments of stenosis). Technological developments in the PI’s lab demonstrate it is feasible to fuse MPI with ICA data. Our recent clinical validation showed that: the number of coronary stenosis segments with uncertainty was significantly reduced by our 3D SPECT-ICA fusion compared with side-by-side analysis; patients who received CR congruent with guidance by 3D fusion had superior outcomes when compared with those who did not. In this proposal, using state-of-the-art machine learning techniques, further technological developments improve the clinical utility of our software system. More specifically, our primary objectives are twofold. First, we use machine-learning-based ICA image processing to reduce our technologies' processing time and minimize user variabilities, making clinical translation feasible. Second, we improve the result interpretation of the SPECT-ICA fusion map using machine-learning methods. More specifically, a 3D SPECT-ICA fusion map can be generated for each patient after multimodality fusion. A new model using the latest reinforcement learning techniques will enhance the interpretation of the SPECT-ICA fusion map and further improve CR outcomes. It is important to note that all the new algorithms and techniques will receive rigorous validations. A retrospective single-center study in a cath lab will be conducted to identify further clinical values of our fusion approach in guiding the CR. Completion of this proposal will not only advance the clinical translation of technologies developed in the PI’s lab, improving the clinical decision-making for patients under consideration for CR, but also enhance Michigan Tech’s research profile by introducing translational cardiology to our students.


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: 2024 ( Subtotal = $427,307 )
2024	2024	MICHIGAN TECHNOLOGICAL UNIVERSITY	1400 TOWNSEND DR	HOUGHTON	MI	49931	HOUGHTON	USA	Cardiovascular Diseases Research	000	1	12/20/2023	NEW	$427,307
														Subtotal = $427,307

Grand Total All Awards = $427,307

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Multi-modality Image Fusion to Improve Coronary Revascularization in Patients with Stable Coronary Artery Disease

Award Number: R15HL172198

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 12/20/2023

PERIOD OF PERFORMANCE END DATE: 11/30/2026

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