Collaborative Research: SCH: Quantifying Cardiac Performance by Measuring Myofiber Strain with Routine MRI

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The goal of this research is to develop a new method to quantify cardiac performance in patients affected by cardiac diseases. Current strategies to evaluate cardiac performance often rely on inadequate global measures, such as ejection fraction, which are non-specific and often late outcomes. Cardiac motion is driven by billions of heart cells acting together, whose contraction and relaxation can be measured using myofiber strain. Myofiber strain is therefore a direct measure of cardiac function and is an ideal candidate to evaluate cardiac performance, improving diagnosis and therapy planning. However, there are three main obstacles that hinder the deployment of myofiber strain in a clinical setting: (i) There is no method to reliably compute myofiber strain from images that are routinely acquired; (ii) There are no reliable error estimates for the evaluated strains, preventing their use to distinguish between health and disease; and (iii) There is no framework to compute myofiber strain on demand without hardware and technical barriers. This project aims at overcoming these obstacles by combining computational modeling and artificial intelligence with readily available magnetic resonance imaging. The transition to the clinic will be highly facilitated by deploying the proposed framework in a completely online platform leveraging on-demand cloud computing. Investigators around the globe will be able to test remotely the newly proposed technology without the need for specific hardware or additional software. The multidisciplinary research carried out in this project will train the next generation of scientists, who will be capable of carrying out projects in smart health and biomedical research at the forefront of medical imaging, artificial intelligence, and computational modeling. The proposed approach will estimate myofiber strain by minimizing the difference between computed and measured surface cardiac motion. Measured surface motion is extracted from cine Magnetic Resonance Imaging (MRI), which is routinely acquired in a clinical MRI setting. Computed left ventricular surface motion is obtained by solving a computational kinematics model based on the biomechanics of myofiber shortening and relaxation. Uncertainty in myofiber strain predictions will be evaluated based on imaging data noise and model assumptions. Fast and accurate high-fidelity models and Bayesian error estimators will propagate experimental and model uncertainties to establish confidence in myofiber strain estimates. As a results, the generated models will allow to characterize strains’ uncertainty and variation in healthy and diseased individuals. The proposed approach will be demonstrated and validated in a pilot study to aid therapy planning in patients affected by aortic stenosis. This new approach paves the way to improve diagnosis, prognosis, and therapy planning for patients affected by a wide range of cardiomyopathies resulting in compromised left ventricular function and therefore myofiber mechanics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.