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DTSTART:20260308T030000
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DTSTAMP:20260301T061648Z
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DTSTART;TZID=America/New_York:20260226T150000
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DESCRIPTION:Cardiovascular diseases are one of the leading causes of death 
 worldwide. We will discuss our group’s recent efforts in using advanced 
 medical imaging and machine-learning based finite-element modeling to bett
 er diagnose and treat various cardiovascular diseases. We will first prese
 nt our group’s recent developments in physics-informed machine-learning 
 that can be used to regularize sparse observational data by embedding know
  physical laws into the loss functions. We will demonstrate the performanc
 e of this framework in patient-specific vascular geometries (e.g.\, aorta\
 , idealized arteries) and subsequently validation in real-world imaging da
 ta. We will also discuss our group's work in advanced medical imaging\, an
 d in particularly\, CT myocardial perfusion imaging to better detect disea
 sed heart vessels\, specifically those that limit blood flow to the heart 
 muscles. Lastly\, we will discuss recent directions within our group to ch
 aracterize durability of tissue-mimicking materials towards advancing and 
 improving bioprosthetic heart valves.\n\nSpeaker(s): Owais\, \n\nVirtual: 
 https://events.vtools.ieee.org/m/542341
LOCATION:Virtual: https://events.vtools.ieee.org/m/542341
ORGANIZER:ajmery.sultana@algomau.ca
SEQUENCE:45
SUMMARY:Cardiovascular Modeling and Simulations to Improve Treatment of Hea
 rt Diseases
URL;VALUE=URI:https://events.vtools.ieee.org/m/542341
X-ALT-DESC:Description: <br /><p>Cardiovascular diseases are one of the lea
 ding causes of death worldwide. We will discuss our group&rsquo\;s recent 
 efforts in using advanced medical imaging and machine-learning based finit
 e-element modeling to better diagnose and treat various cardiovascular dis
 eases. We will first present our group&rsquo\;s recent developments in phy
 sics-informed machine-learning that can be used to regularize sparse obser
 vational data by embedding know physical laws into the loss functions. We 
 will demonstrate the performance of this framework in patient-specific vas
 cular geometries (e.g.\, aorta\, idealized arteries) and subsequently vali
 dation in real-world imaging data. We will also discuss our group's work i
 n advanced medical imaging\, and in particularly\, CT myocardial perfusion
  imaging to better detect diseased heart vessels\, specifically those that
  limit blood flow to the heart muscles. Lastly\, we will discuss recent di
 rections within our group to characterize durability of tissue-mimicking m
 aterials towards advancing and improving bioprosthetic heart valves.</p>
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