InvitedTalkMansi2012

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Invited Talk by Tommaso Mansi

  • Date: Friday, May 11th, 2012
  • Time: 09:00am - 10:00am
  • Location: MI 03.13.010

Data-Driven Computational Models of Heart Anatomy, Mechanics and Hemodynamics

Abstract:

Patient-specific therapy planning is challenging due to the large variability in disease causes, locations and severity. A predictive framework is therefore needed to assess the optimal therapy for a specific patient in terms of maximizing effectiveness and minimizing the risk of complications. We present an integrated framework for multi-physics heart modeling based on medical images. Our approach relies on efficient machine-learning methods to estimate an accurate and comprehensive model of patient’s anatomy from MRI. A computational model of cardiac electrophysiology and biomechanics is then solved on patient’s anatomy. The simulated cardiac motion is then provided as input to a computational fluid dynamics (CFD)-based model to compute intra-cardiac blood flow throughout the cardiac cycle. Experiments obtained with our framework will be presented.

Speaker:

Bernt Schiele Dr. Tommaso Mansi is research scientist at Siemens Corporation, Corporate Research and Technology in the Image Analytics and Informatics technology field. He received his Ph.D. degree in biomedical engineering from Ecole des Mines de Paris, Sophia-Antipolis, France in 2010 as a result of his research activity at INRIA Sophia Antipolis, Asclepios Research Team (Dr. Nicholas Ayache). During his Ph.D. at INRIA, Tommaso research topics were mainly in computational modeling of heart function, image registration and statistical shape analysis. At Siemens Corporation, Tommaso is currently investigating computational models of cardiovascular physiology for advanced diagnostics and therapy planning.

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Title: Invited Talk by Dr. Tommaso Mansi
Date: 11 May 2012
Location: MI 03.13.010
Abstract: Patient-specific therapy planning is challenging due to the large variability in disease causes, locations and severity. Dr. Tommaso Mansi is going to present an integrated framework for multi-physics heart modeling based on medical images, relying on efficient machine-learning methods to estimate an accurate and comprehensive model of patient’s anatomy from MRI
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