Computational Modeling and Deep Learning of Heart Tissue Dynamics: Studying the Interplay between Excitation Waves, Calcium, and Mechanical Contraction
Jan Christoph, Professor
UC San Francisco
Applications for Spring 2025 are closed for this project.
Our group studies heart rhythm disorders, such as ventricular tachycardia or atrial fibrillation, using computer simulations and imaging. Heart rhythm disorders are associated with abnormal electrophysiological excitation wave phenomena in the heart muscle, which can take on complex pattern-forming and self-organizational spatio-temporal dynamics. The excitation triggers intracellular calcium release which is involved in the contraction of heart muscle cells. Thus, the electrical excitation patterns cause complicated mechanical deformation patterns which we analyze in imaging data. In one of our recent studies, we have shown how deep learning can in principle be used to compute electrophysiological phenomena from the motion and deformation of the heart: https://arxiv.org/abs/2305.07822. This inverse computational approach could be used for diagnostic imaging of heart rhythm disorders based on ultrasound or magnetic resonance imaging.
Role: The goal of this project is to develop computational methods for the modeling of spatio-temporal tissue dynamics in the heart with a focus on the coupling between excitation, calcium, and mechanics. The student can build on and extend existing algorithms and software and will carry out method development for performing simulations, generating training samples for deep learning and, developing deep learning models. The work will be based on the following publication: https://pubs.aip.org/aip/cha/article/30/12/123134/282802/Inverse-mechano-electrical-reconstruction-of
In this project, the student will study ways in which the coupling between excitation and calcium can degenerate and lead to rhythm disturbances from the cellular to the tissue scale.
Qualifications: Applicants should have a background and interest in mathematical biology, biophysics / biological physics, or computational modeling, and eventually deep learning. Experience with at least one programming language (e.g. Python, Matlab, C/C++, etc.) is required. At least 12 hours are typically needed to make significant progress and students who can devote at least 12 hours will be given preference.
Day-to-day supervisor for this project: Jan Christoph, Staff Researcher
Hours: 12 or more hours
Off-Campus Research Site: The work will be performed at our institute, the Cardiovascular Research Institute on UCSF's Mission Bay Campus on 1-2 days a week. With increasing experience and independence (4+ months), the student may also work from home. It is required to participate in a 1-2h long lab meeting every 2 weeks on Thursdays between 4-6pm, join work-related discussions on Discord as well as regular 1-on-1 meetings once per week to discuss the student's progress either in person or on Zoom. Please state your availability (which weekdays work best for you) when applying.
Related website: http://cardiacvision.ucsf.edu
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