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
2012
Volumetric Intraoperative Brain Deformation Compensation: Model Development and Phantom Validation
DeLorenzo C, Papademetris X, Staib LH, Vives KP, Spencer DD, Duncan JS. Volumetric Intraoperative Brain Deformation Compensation: Model Development and Phantom Validation. IEEE Transactions On Medical Imaging 2012, 31: 1607-1619. PMID: 22562728, PMCID: PMC3600363, DOI: 10.1109/tmi.2012.2197407.Peer-Reviewed Original ResearchConceptsLinear elastic modelSurface displacementsElastic modelBrain deformationVolumetric brain deformationMaterial parametersMaterial propertiesRealistic brain phantomDeformationBiomechanical modelIntraoperative brainModel accuracyLocalization errorPhantom validationAccurate surgical guidanceModel solutionsModel developmentInitial estimationDisplacementModel sensitivityQuantitative validationPhantom resultsPreoperative imagesSurgical guidancePreliminary application