Machine learning–based 3‐D geometry reconstruction and modeling of aortic valve deformation using 3‐D computed tomography images
Liang L, Kong F, Martin C, Pham T, Wang Q, Duncan J, Sun W. Machine learning–based 3‐D geometry reconstruction and modeling of aortic valve deformation using 3‐D computed tomography images. International Journal For Numerical Methods In Biomedical Engineering 2016, 33 PMID: 27557429, PMCID: PMC5325825, DOI: 10.1002/cnm.2827.Peer-Reviewed Original ResearchConceptsHuman expertsGeometry reconstructionHuman errorMean discrepancyPreoperative planning systemComputational modeling processReconstructed geometryFinite element model generationModel generationPatient-specific computational modelingCardiac imagesComputational modeling methodsFast feedbackComputational modeling frameworkModeling processMesh correspondencePlanning systemModeling methodMachineModeling frameworkAortic valveImagesDisease diagnosisLarge patient cohortIndividual patient needsTowards 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