Events
PhD DefenseAI-Driven Correspondence Learning for Dynamic Heart Function Analysis Using MRI |
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Monday, May 19, 2025, 11:00am - 01:00pm |
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Speaker: Meng Ye
Bio
Location : CoRE 301
Committee:
Professor Dimitris N. Metaxas
Professor Ahmed Elgammal
Associate Professor Desheng Zhang
Professor Daniel Bruce Ennis (external)
Event Type: PhD Defense
Abstract: Heart disease remains a leading cause of disability and death worldwide, with various conditions adversely impacting heart function and remodeling its structures in diverse ways, leading to significant clinical consequences. Cardiac cine MRI, the gold standard for assessing heart function, is limited by inherently slow imaging speeds. Currently, most clinical cine MRI protocols are still based on 2D imaging, requiring patients to hold their breath during scans. The resulting 2D stack images often require complex post-processing, which can still lead to inaccurate and oversimplified biomarker measurements.In this defense, I will present my work on advanced AI methods for high-dimensional dynamic heart function analysis using conventional and tagged cine MRI through correspondence learning. First, I will introduce continuous spatial-temporal memory networks for 4D cardiac cine MRI segmentation. Next, I will demonstrate how neural deformable models can reconstruct 3D heart wall geometry from sparsely sampled cine MRI data. I will then explain how unsupervised learning-based image registration networks, inspired by physics, can estimate in-plane cardiac wall motion from image sequences. Finally, I will discuss volumetric neural deformable models for recovering 3D regional heart wall motion from 2D motion cues provided by tagged MRI.The defense will conclude with a vision for AI-augmented methods that have the potential to reshape the future of heart disease care.
Organization:
Contact Professor Dimitris N. Metaxas
Zoom Link
https://rutgers.zoom.us/j/96514935479?pwd=CqTFAicbFkuLX4ibtaQYb21zFjYTD9.1