Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps

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dc.catalogadorgjm
dc.contributor.authorRuiz Herrera, Carlos
dc.contributor.authorGrandits, Thomas
dc.contributor.authorPlank, Gernot
dc.contributor.authorPerdikaris, Paris
dc.contributor.authorSahli Costabal, Francisco
dc.contributor.authorPezzuto, Simone
dc.date.accessioned2024-05-30T16:23:25Z
dc.date.available2024-05-30T16:23:25Z
dc.date.issued2022
dc.description.abstractWe propose FiberNet, a method to estimate in-vivo the cardiac fiber architecture of the human atria from multiple catheter recordings of the electrical activation. Cardiac fibers play a central role in the electro-mechanical function of the heart, yet they are difficult to determine in-vivo, and hence rarely truly patient-specific in existing cardiac models. FiberNet learns the fiber arrangement by solving an inverse problem with physics-informed neural networks. The inverse problem amounts to identifying the conduction velocity tensor of a cardiac propagation model from a set of sparse activation maps. The use of multiple maps enables the simultaneous identification of all the components of the conduction velocity tensor, including the local fiber angle. We extensively test FiberNet on synthetic 2-D and 3-D examples, diffusion tensor fibers, and a patient-specific case. We show that 3 maps are sufficient to accurately capture the fibers, also in the presence of noise. With fewer maps, the role of regularization becomes prominent. Moreover, we show that the fitted model can robustly reproduce unseen activation maps. We envision that FiberNet will help the creation of patient-specific models for personalized medicine. The full code is available at http://github.com/fsahli/FiberNet.
dc.fechaingreso.objetodigital2024-09-27
dc.fuente.origenORCID
dc.identifier.doi10.1007/s00366-022-01709-3
dc.identifier.urihttps://doi.org/10.1007/s00366-022-01709-3
dc.identifier.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-85134614244&partnerID=MN8TOARS
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86077
dc.identifier.wosidWOS:000828459800001
dc.information.autorucEscuela de Ingeniería; Sahli Costabal, Francisco; S/I; 154857
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final3973
dc.pagina.inicio3957
dc.revistaEngineering with Computers
dc.rightsacceso restringido
dc.subjectCardiac fibers
dc.subjectPhysics-informed neural networks
dc.subjectCardiac electrophysiology
dc.subjectAnisotropic conduction velocity
dc.subjectEikonal equation
dc.subjectDeep learning
dc.subject.ddc600
dc.subject.deweyTecnologíaes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titlePhysics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps
dc.typeartículo
dc.volumen38
sipa.codpersvinculados154857
sipa.trazabilidadORCID;2024-05-27
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