<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Grandits, Thomas</dc:creator>
  <dc:creator>Effland, Alexander</dc:creator>
  <dc:creator>Pock, Thomas</dc:creator>
  <dc:creator>Krause, Rolf</dc:creator>
  <dc:creator>Plank, Gernot</dc:creator>
  <dc:creator>Pezzuto, Simone</dc:creator>
  <dc:date>2021-06-25</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">The identification of the initial ventricular activation sequence is a critical step for the correct personalization of patient- specific cardiac models. In healthy conditions, the Purkinje network is the main source of the electrical activation, but  under pathological conditions the so-called earliest activation sites (EASs) are possibly sparser and more localized. Yet,  their number, location and timing may not be easily inferred from remote recordings, such as the epicardial activation or  the 12-lead electrocardiogram (ECG), due to the underlying complexity of the model. In this work, we introduce GEASI  (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify all EASs. To this  end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton–Jacobi  formulation to minimize a given objective function, for example, the quadratic mismatch to given activation  measurements. This versatile approach can be extended to estimate the number of activation sites by means of the  topological gradient, or fitting a given ECG. We conducted various experiments in 2D and 3D for in-silico models and an  in-vivo intracardiac recording collected from a patient undergoing cardiac resynchronization therapy. The results  demonstrate the clinical applicability of GEASI for potential future personalized models and clinical intervention.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://n2t.net/ark:/12658/srd1319302</dc:identifier>
  <dc:identifier>https://susi.usi.ch/global/documents/319302</dc:identifier>
  <dc:identifier>https://susi.usi.ch/documents/319302/files/Pezzuto_ijnmbe_2021.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1002/cnm.3505</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/ark/12658/srd1319302</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>CC BY</dc:rights>
  <dc:source>International journal for numerical methods in biomedical engineering. - Wiley. - 2021, vol. 37, no. 8, p. 30</dc:source>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Cardiac model personalization</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">Earliest activation sites</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">Eikonal equation : Hamilton–Jacobi</dc:subject>
  <dc:subject xmlns:ns4="xml" ns4:lang="en">Formulation</dc:subject>
  <dc:subject xmlns:ns5="xml" ns5:lang="en">Inverse ECG problem</dc:subject>
  <dc:subject xmlns:ns6="xml" ns6:lang="en">Topological gradient</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/61</dc:subject>
  <dc:title xmlns:ns7="xml" ns7:lang="en">GEASI : Geodesic-based earliest activation sites identification in cardiac models</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
