Weihua Zhou, Applied Computing is the PI on a project that received a $427,307 research and development grant from the National Institutes of Health.
The project is titled “Multi-modality Image Fusion to Improve Coronary Revascularization in Patients with Stable Coronary Artery Disease.”
Jingfeng Jiang, Biomedical Engineering, is a co-PI on this potential three-year project.
Zhou and Jiang are members of the Institute of Computing and Cybersystems (ICC).
Dr. Zhou is looking for undergraduate student research assistants with a computer science/ computer engineering background. Hourly pay will be offered. Students will learn advanced skills in deep learning for medical image analysis and computer vision. Please contact Dr. Zhou at whzhou@mtu.edu.
Project Abstract
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.