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Professor Shontz Receives NSF Support to Develop a Novel Computational Framework for Cardiac Biomechanics

December 4, 2018

The National Science Foundation is supporting Electrical Engineering and Computer Science Professor Suzanne Shontz and her colleagues at Rochester Institute of Technology to develop a novel computational framework for cardiac biomechanics. A normal heart functions by contracting and pushing blood from the left ventricle into the rest of the body. Due to various diseases, the contraction capabilities of the heart become diminished in certain regions of the heart chamber wall, compromising the organ’s overall function. In order to identify and select optimal treatment, it is critical to identify the regions of the heart muscle that exhibit reduced contractions. Unfortunately, contractions cannot be easily measured. This project will estimate the stress (contraction power) developed within the heart muscle by combining medical imaging and mechanical modeling of the heart. These stresses will serve as a quantitative measure of the contractile function and help detect and localize disease. Therefore, this research has the potential to evolve into a future tool to diagnose cardiac function.

The goal is to enable noninvasive appraisal and visualization of the active stresses developed in the myocardium to serve as a direct means to assess the biomechanical function of the heart. The team will develop a novel computational framework for cardiac biomechanics to reconstruct the active stresses from cardiac deformations. This framework will integrate techniques for medical image computing, high order meshing and inverse-problem biomechanical modeling, developed and validated as a collaborative effort between three scientists and their laboratories from KU and the Rochester Institute of Technology.

This collaborative project and grant proposal has its genesis at the 2015 ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE 2015) where Professor Shontz and Christian Linte, assistant professor of mechanical and materials engineering at the Rochester Institute of Technology, met for the first time. Both were speakers in the scientific program of the conference. Their complementary expertise – Professor Shontz’s expertise in high-order meshing and scientific computing and Assistant Professor Linte’s expertise in medical imaging and image computing for computer-assisted diagnosis and therapy applications – fueled an ongoing collaboration, which later encompassed Rochester Institute of Technology Associate Professor of Mathematical Sciences Nies Otani – an expert in inverse problems -- and led to a joint publication in VipIMAGE 2017 and a joint talk at Coupled Problems 2017.

To assist with the clinical expertise and access to retrospective clinical data for the overall validation of the computational infrastructure, the team will leverage existing RIT collaborations with cardiologists at the University of Rochester Medical Center. In addition, asst. prof. Linte also has an ongoing collaboration with Kitware, a leader in scientific computing and visualization, which will contribute its expertise in open-source software development and provide internship opportunities for students on the team.

This research will address a currently unexplored niche in the cardiac modeling field, specifically the reconstruction and visualization of myocardial active stresses, to enable direct appraisal of cardiac function. The team will contribute to medical image computing through the development of algorithms for medical image processing and visualization. This research will contribute new knowledge in mathematical modeling and simulation by implementing efficient nonlinear least-squares solutions for inverse cardiac biomechanics.

 

Cut-away views of tetrahedral volume meshes of the myocardium [1].  The cyan elements correspond to triangles on the surface mesh, whereas the magenta elements correspond to tetrahedra in the volume mesh.  The research team will use such low-order meshes to generate higher-order meshes which will represent the cardiac geometry more efficiently. [From: Niels F. Otani, Dylan Dang, Shusil Dangi, Mike Stees, Suzanne M. Shontz, and Cristian A. Linte, Assessing cardiac tissue function via action potential wave imaging using cardiac displacement data, Proc. of the VI ECCOMAS Thematics Conference on Computational Vision and Medical Image Processing (VipIMAGE 2017), Lecture Notes in Computational Vision and Biomechanics, Volume 27, pp. 903-912, 2017.]



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