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CS Lecture Series: Dr. Jingfeng Jiang

This is a past event.

Friday, October 1, 2021 3:00pm

This is a past event.

Dr. Jingfeng Jiang, associate professor, Biomedical Engineering, will present a Computer Science department lecture on Friday, October 1, 2021, at 3:00 p.m. in Rekhi 214. The talk is titled, "Translating Biomechanical Imaging Into Clinical Workflow: Experience and Insight."

Dr. Jiang’s research work straddles the border between biomechanics and biomedical imaging. He is actively developing image-based computational tools for easily transforming raw biomedical imaging data into simple but clinically/biologically relevant biomechanical parameters of soft tissue pathologies (e.g. elasticity) and blood flow characteristics (e.g. kinetic energy).

TALK ABSTRACT: Disease evolution is often accompanied by changes in biomechanical properties of biological tissues. Thus, there is a growing need to quantify biomechanical properties of in vivo human tissues. Unfortunately, standard medical imaging modalities such as CT, MR, and Ultrasound cannot provide such information.

Biomechanical imaging (also known as elastography) is a derivative of medical (ultrasound) imaging and can be used to non-invasively estimate tissue elasticity. In recent years, biomechanical imaging has been used for a growing number of clinical applications (e.g. cancer detection and differentiation, staging liver fibrosis, etc.).<br>In this talk, I will describe my experience in computer algorithm design for biomechanical imaging, as well as my effort in translating biomechanical imaging to the bedside. Also, I will discuss future technical challenges and research opportunities.

His research interests are biomechanics, automated control of ultrasound scanning including the use of 3D printing technology, image and signal processing, non-invasive assessment of biomechanical properties of soft tissues, and computer aided analyses of cardiovascular flow. He is a member of the Institute of Computing and Cybersystems’s (ICC) Joint Center for Biocomputing and Digital Health research group.

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  • Alireza Asadbeygi

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