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DTSTAMP:20210402T160023Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201117T083000
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UID:submissions.supercomputing.org_SC20_sess342_drs105@linklings.com
SUMMARY:Establishing a Massively Parallel, Patient-Specific Model of Cardi
 ovascular Disease
DESCRIPTION:Doctoral Showcase, Posters\n\nEstablishing a Massively Paralle
 l, Patient-Specific Model of Cardiovascular Disease\n\nVardhan, Randles\n\
 nRecent years have witnessed a dramatic increase in computational fluid dy
 namic (CFD) simulations for diagnosing cardiovascular diseases, which cont
 inue to dominate healthcare costs and are projected to be over one trillio
 n dollars by 2035. Current frameworks, however, face three key technical c
 hallenges: simulations are memory intensive with high time-to-solutions; t
 he need for validation against in vivo measurements; and methods for clini
 cians to intuitively interact with the simulation results are lacking. In 
 this thesis, we overcome these challenges by first establishing a novel, m
 emory-light algorithmic representation that both reduces the memory requir
 ements by 74% and maintains excellent parallel scalability. Second, we val
 idate our CFD framework through a multicenter, clinical study comparing in
 vasive pressure measurements to calculated values for 200 patients. Third,
  we assess how physicians interact with large-scale CFD simulation data an
 d present a virtual reality platform to enhance treatment planning. We exp
 ect this work to lay the critical groundwork for translating the use of ma
 ssively parallel simulation-driven diagnostics and treatment planning to t
 he clinic. Our long-term goal is to enable the use of personalized simulat
 ions to improve clinical diagnosis and outcome for patients suffering from
  cardiovascular diseases.\n\nRegistration Category: Tech Program Reg Pass,
  Exhibits Reg Pass
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