Coordinate-Independent 3-D Ultrasound Principal Stretch and Direction Imaging
Jeng G, Chen P, Hsieh M, Liu Z, Langdon J, Ahn S, Staib L, Stendahl J, Thorn S, Sinusas A, Duncan J, O'Donnell M. Coordinate-Independent 3-D Ultrasound Principal Stretch and Direction Imaging. IEEE Transactions On Biomedical Engineering 2024, 71: 3312-3323. PMID: 38941195, DOI: 10.1109/tbme.2024.3420220.Peer-Reviewed Original ResearchPrincipal stretchesAxial displacement componentsSpeckle tracking methodSpeckle tracking approachTracking methodRobust filterDisplacement componentsTissue incompressibilityDisplacement estimationStrain componentsDisplacement gradientsStrain informationLocalized diseased regionStrain imagingLagrangian strainLeast-squares methodTracking approachTracking frameworkCoordinate systemCardiac coordinate systemProbe orientationFilterHigher spatial resolutionCardiac datasetsEnhanced accuracyHeteroscedastic Uncertainty Estimation Framework for Unsupervised Registration
Zhang X, Pak D, Ahn S, Li X, You C, Staib L, Sinusas A, Wong A, Duncan J. Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration. Lecture Notes In Computer Science 2024, 15002: 651-661. DOI: 10.1007/978-3-031-72069-7_61.Peer-Reviewed Original ResearchUnsupervised registrationReal-world medical imagesCollaborative training strategyMedical image datasetsDeep learning methodsAccurate displacement estimationSignal-to-noise ratioImage datasetsRegistration architectureLearning methodsMedical imagesTraining strategyNoise distributionUncertainty estimationWeighting schemeRegistration performanceSpatial domainEstimation frameworkInput-dependentUncertainty estimation frameworkUniform noise levelsDisplacement estimationFrameworkNoise levelUnsupervisedAdaptive Correspondence Scoring for Unsupervised Medical Image Registration
Zhang X, Stendahl J, Staib L, Sinusas A, Wong A, Duncan J. Adaptive Correspondence Scoring for Unsupervised Medical Image Registration. Lecture Notes In Computer Science 2024, 15096: 76-92. DOI: 10.1007/978-3-031-72920-1_5.Peer-Reviewed Original ResearchMedical image registrationAdaptation frameworkMedical image datasetsUnsupervised learning schemeAdaptive training schemeImage registrationError residualsSupervision signalsLearning schemeImage datasetsRegistration architectureIntensity constancyScore mapNoisy gradientsMedical imagesTraining schemeImage reconstructionPerformance degradationLambertian assumptionCorrespondence scoresLoss of correspondenceTraining objectivesDisplacement estimationImage acquisitionScheme