2024
Medical image registration via neural fields
Sun S, Han K, You C, Tang H, Kong D, Naushad J, Yan X, Ma H, Khosravi P, Duncan J, Xie X. Medical image registration via neural fields. Medical Image Analysis 2024, 97: 103249. PMID: 38963972, DOI: 10.1016/j.media.2024.103249.Peer-Reviewed Original ResearchLearning-based methodsNeural fieldsNeural networkImage registrationMedical image analysis tasksMini-batch gradient descentImage analysis tasksDeep neural networksMedical image registrationDiffeomorphic image registrationImage registration frameworkOptimization-based methodDomain shiftAnalysis tasksGradient descentCompetitive performanceImage pairsRegistration taskOptimal deformationShort computation timeRegistration frameworkDesign choicesDisplacement vector fieldComputation timeModel optimization
2007
Boundary element method-based regularization for recovering of LV deformation
Yan P, Sinusas A, Duncan JS. Boundary element method-based regularization for recovering of LV deformation. Medical Image Analysis 2007, 11: 540-554. PMID: 17584521, DOI: 10.1016/j.media.2007.04.007.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsComputer SimulationDogsElasticityFinite Element AnalysisHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingModels, CardiovascularReproducibility of ResultsSensitivity and SpecificityStress, MechanicalVentricular Dysfunction, LeftConceptsBoundary element methodImage sequencesElement methodDisplacement fieldDense displacement fieldNew regularization modelDeformationRegularization modelCardiac magnetic resonance image sequencesMagnetic resonance image sequencesEchocardiographic image sequencesLattice densityFeature informationComputation timePhysical plausibilityImage dataDisplacementMatching strategy