2022
Learning Correspondences of Cardiac Motion from Images Using Biomechanics-Informed Modeling
Zhang X, You C, Ahn S, Zhuang J, Staib L, Duncan J. Learning Correspondences of Cardiac Motion from Images Using Biomechanics-Informed Modeling. Lecture Notes In Computer Science 2022, 13593: 13-25. DOI: 10.1007/978-3-031-23443-9_2.Peer-Reviewed Original ResearchDisplacement vector fieldQuantitative evaluation metricsCardiac anatomical structuresTraining complexityExtensive experimentsSegmentation performanceBiomechanical feasibilityEvaluation metricsAvailable datasetsCardiac motionSmoothness constraintGeometric constraintsBiomechanical propertiesDatasetMRI dataPlausible transformationsMotionConstraintsAnatomical structuresRegularizerRegularization schemeMethodIncompressibilityImagesMetrics
2004
Geometric strategies for neuroanatomic analysis from MRI
Duncan JS, Papademetris X, Yang J, Jackowski M, Zeng X, Staib LH. Geometric strategies for neuroanatomic analysis from MRI. NeuroImage 2004, 23: s34-s45. PMID: 15501099, PMCID: PMC2832750, DOI: 10.1016/j.neuroimage.2004.07.027.Peer-Reviewed Original ResearchConceptsApplied mathematical approachWhite matter fiber tracksStatistical estimationMathematical approachFunction-structure analysisMagnetic resonance imagesEvolution strategyGeometric constraintsImage processingIntersubject registrationRich setGeometric strategyOngoing workData setsUse of levelsCommon spaceNeuroanatomic analysisSetRegistrationFiber tracksHuman brainResonance imagesInformationSegmentationEstimation