2024
Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning
Pak D, Liu M, Kim T, Liang L, Caballero A, Onofrey J, Ahn S, Xu Y, McKay R, Sun W, Gleason R, Duncan J. Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning. IEEE Transactions On Medical Imaging 2024, 43: 203-215. PMID: 37432807, PMCID: PMC10764002, DOI: 10.1109/tmi.2023.3294128.Peer-Reviewed Original ResearchMeSH KeywordsBiomechanical PhenomenaComputer SimulationDeep LearningHeartHumansPatient-Specific ModelingConceptsFinite element analysisDeep learning methodsSpatial accuracyElement analysisDeep learningStress estimationLearning methodsSimulation accuracyDeployment simulationHigh spatial accuracyThin structuresMesh generationVolumetric meshingDeformation energyGeometry modelingVolumetric meshMesh qualityElement qualitySimultaneous optimizationMain noveltyBiomechanics studiesMeshModeling characteristicsAccuracyDownstream analysis
2016
Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation
Zhang F, Kanik J, Mansi T, Voigt I, Sharma P, Ionasec RI, Subrahmanyan L, Lin BA, Sugeng L, Yuh D, Comaniciu D, Duncan J. Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation. Medical Image Analysis 2016, 35: 599-609. PMID: 27718462, DOI: 10.1016/j.media.2016.09.006.Peer-Reviewed Original ResearchConceptsMitral valve modelingTemporal informationPatient-specific modelingImage acquisitionEuclidean distanceValve modelingComputational frameworkExtended Kalman filterImage analysisModeling frameworkKalman filterFrameworkAverage errorMitral valve geometryTEE imagesInformationMachineParameter estimationClosed mitral valveLeaflet material propertiesSubjective predictionModelingImagesRepresentationOptimization