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 ResearchConceptsFinite 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
2021
Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images
Pak D, Liu M, Ahn S, Caballero A, Onofrey J, Liang L, Sun W, Duncan J. Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images. Lecture Notes In Computer Science 2021, 12729: 637-648. DOI: 10.1007/978-3-030-78191-0_49.Peer-Reviewed Original ResearchSupervised deep learningTranscather aortic valve replacementDeep learningSegmentation labelsMesh generationCorrespondence accuracyHeavy assumptionsFinite element mesh generationMesh topologyVolumetric meshLow contrastSignificant bottleneckValve modelingProblem formulationPrediction modelModel performanceCT imagesDeformation strategyLarge amountImagesBottleneckLearningMeshFrameworkLabels