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DTSTAMP:20230831T095746Z
LOCATION:Sanada I
DTSTART;TZID=Europe/Stockholm:20230627T160000
DTEND;TZID=Europe/Stockholm:20230627T163000
UID:submissions.pasc-conference.org_PASC23_sess174_msa263@linklings.com
SUMMARY:Automatic Model Construction for Patient-Specific Aortic Flow Simu
 lations Using Geometric Deep Learning
DESCRIPTION:Minisymposium\n\nPan Du, Delin An, Chaoli Wang, and Jian-Xun W
 ang (University of Notre Dame)\n\nImage-based computational fluid dynamics
  (CFD) provides comprehensive hemodynamic flow information and has hence b
 een widely used in cardiovascular disease diagnosis. Reliable CFD simulati
 on result requires accurate reconstruction of the geometry from medical im
 ages such as computerized tomography or magnetic resonance imaging. The tr
 aditional reconstruction approach involves manual operation which induces 
 artifacts and is time-inefficient. Recent development in artificial intell
 igence inspires a series of works applying machine learning algorithms or 
 deep neural networks (DNN) to automate this process, where 2-D or 3-D DNNs
  are used to map from medical image data to the true geometry. However, th
 ose methods either are not fully automated (e.g., require centerlines or m
 anual pre/post-processing) or suffer from surface irregularity, especially
  for complicated geometries (e.g., the vascular tree vessel structure). To
  fill the gap, we proposed an automatic geometry reconstruction algorithm 
 for patient-specific aortic flow simulation. The model uses a 3-D U-net wi
 th a shape stream module to predict a pixel volume from raw medical images
 , from which a template geometry is reconstructed and deformed to the true
  geometry using a graph deformation module. After training on the Vascular
  Model Repository (VMR) dataset, our model outperforms state-of-art models
  and has great generalizability across different patients.\n\nDomain: Life
  Sciences\n\nSession Chair: Georgios Kissas (University of Pennsylvania)
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