2024
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 accuracy
2022
Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography
Ahn S, Ta K, Thorn S, Onofrey J, Melvinsdottir I, Lee S, Langdon J, Sinusas A, Duncan J. Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography. Medical Image Analysis 2022, 84: 102711. PMID: 36525845, PMCID: PMC9812938, DOI: 10.1016/j.media.2022.102711.Peer-Reviewed Original ResearchConceptsSpatial transformer networkMotion trackingNoisy displacement fieldReliable motion estimationMotion tracking methodCardiac strain analysisTransformer networkDisplacement fieldDisplacement pathsMotion fieldTracking methodMotion estimationExperimental resultsStrain analysisSuperior performanceTemporal constraintsCardiac motionTrackingRegularization functionDependent featuresEchocardiography imagesNetworkPrior assumptionsField
2017
Flow Network Based Cardiac Motion Tracking Leveraging Learned Feature Matching
Parajuli N, Lu A, Stendahl J, Zontak M, Boutagy N, Alkhalil I, Eberle M, Lin B, O’Donnell M, Sinusas A, Duncan J. Flow Network Based Cardiac Motion Tracking Leveraging Learned Feature Matching. Lecture Notes In Computer Science 2017, 10434: 279-286. DOI: 10.1007/978-3-319-66185-8_32.Peer-Reviewed Original ResearchCardiac motion trackingNeural networkMotion trackingTedious feature engineeringSiamese neural networkMotion tracking methodFeature engineeringSiamese networkFeature matchingGraph nodesImage patchesSpatiotemporal problemsTracking algorithmTracking methodEdge weightsNetworkLinear programmingConsistent constraintsAdditional important contributionTrackingAlgorithmDatasetProgrammingNodesMatchingLearning-Based Spatiotemporal Regularization and Integration of Tracking Methods for Regional 4D Cardiac Deformation Analysis
Lu A, Zontak M, Parajuli N, Stendahl J, Boutagy N, Eberle M, Alkhalil I, O’Donnell M, Sinusas A, Duncan J. Learning-Based Spatiotemporal Regularization and Integration of Tracking Methods for Regional 4D Cardiac Deformation Analysis. Lecture Notes In Computer Science 2017, 10434: 323-331. DOI: 10.1007/978-3-319-66185-8_37.Peer-Reviewed Original ResearchDeformation analysisTracking methodCardiac motion trackingDeformation-based methodUltrasound imaging propertiesSurface trackingGood overall estimationCardiac deformation analysisMotion trackingSpatiotemporal regularizationInput sourcesDifferent input sourcesTracking resultsTrackingPerceptron networkRegularization procedureMulti-layered perceptron networkMethod
2013
4-D echocardiography assessment of local myocardial strain using 3-D speckle tracking combined with shape tracking
Wong E, O'Donnell M, Thiele K, Comnas C, Huang X, Sampath S, Lin B, Pal P, Papademetris X, Dione D, Staib L, Sinusas A, Duncan J. 4-D echocardiography assessment of local myocardial strain using 3-D speckle tracking combined with shape tracking. 2013, 100-103. DOI: 10.1109/ultsym.2013.0026.Peer-Reviewed Original Research