2020
Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation
Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annual Review Of Biomedical Engineering 2020, 22: 1-27. PMID: 32169002, PMCID: PMC9351438, DOI: 10.1146/annurev-bioeng-060418-052147.Peer-Reviewed Original ResearchRegional myocardial strain analysis via 2D speckle tracking echocardiography: validation with sonomicrometry and correlation with regional blood flow in the presence of graded coronary stenoses and dobutamine stress
Stendahl JC, Parajuli N, Lu A, Boutagy NE, Guerrera N, Alkhalil I, Lin BA, Staib LH, O’Donnell M, Duncan JS, Sinusas AJ. Regional myocardial strain analysis via 2D speckle tracking echocardiography: validation with sonomicrometry and correlation with regional blood flow in the presence of graded coronary stenoses and dobutamine stress. Cardiovascular Ultrasound 2020, 18: 2. PMID: 31941514, PMCID: PMC6964036, DOI: 10.1186/s12947-019-0183-x.Peer-Reviewed Original ResearchConceptsSpeckle tracking echocardiographyLow-dose dobutamine stressPost-systolic indexDobutamine stressCoronary stenosisBlood flow measurementsTracking echocardiographyAnesthetized open-chest dogsCircumferential strainLow-dose dobutamineMid-left anteriorModerate coronary stenosisOpen-chest dogsRegional blood flowAcute canine modelMyocardial strain analysisMicrosphere blood flow measurementsLAD stenosis
2019
Flow network tracking for spatiotemporal and periodic point matching: Applied to cardiac motion analysis
Parajuli N, Lu A, Ta K, Stendahl J, Boutagy N, Alkhalil I, Eberle M, Jeng GS, Zontak M, O'Donnell M, Sinusas AJ, Duncan JS. Flow network tracking for spatiotemporal and periodic point matching: Applied to cardiac motion analysis. Medical Image Analysis 2019, 55: 116-135. PMID: 31055125, PMCID: PMC6939679, DOI: 10.1016/j.media.2019.04.007.Peer-Reviewed Original ResearchConceptsDeformation/strainExcellent tracking accuracyEntire cardiac cycleTracking accuracyCardiac motion analysisAccurate estimationSurface pointsEchocardiographic image sequencesLV motionDisplacementMotion analysisImage sequencesCardiac cyclePoint matchingMotionConsecutive framesEstimationNetwork trackingImportant characteristicsSignificant promiseSchemeGood correlationFlow
2014
Radial Basis Functions for Combining Shape and Speckle Tracking in 4D Echocardiography
Compas C, Wong EY, Huang X, Sampath S, Lin BA, Pal P, Papademetris X, Thiele K, Dione DP, Stacy M, Staib LH, Sinusas AJ, O'Donnell M, Duncan JS. Radial Basis Functions for Combining Shape and Speckle Tracking in 4D Echocardiography. IEEE Transactions On Medical Imaging 2014, 33: 1275-1289. PMID: 24893257, PMCID: PMC4283552, DOI: 10.1109/tmi.2014.2308894.Peer-Reviewed Original Research
2013
Contour tracking in echocardiographic sequences via sparse representation and dictionary learning
Huang X, Dione DP, Compas CB, Papademetris X, Lin BA, Bregasi A, Sinusas AJ, Staib LH, Duncan JS. Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. Medical Image Analysis 2013, 18: 253-271. PMID: 24292554, PMCID: PMC3946038, DOI: 10.1016/j.media.2013.10.012.Peer-Reviewed Original ResearchConceptsContour trackerSparse representationEchocardiographic sequencesRegion-based level set segmentationLevel set segmentationLocal image appearanceManual tracingExpert manual tracingsMultiscale sparse representationImage sequencesSegmentation resultsAppearance modelSpatiotemporal priorsFirst frameMultilevel informationHuman data setsEjection fraction estimatesLocal appearanceImage appearanceDictionary learningShape modelContour trackingManual resultsData setsContour estimationSegmentation of 4D Echocardiography Using Stochastic Online Dictionary Learning
Huang X, Dione DP, Lin BA, Bregasi A, Sinusas AJ, Duncan JS. Segmentation of 4D Echocardiography Using Stochastic Online Dictionary Learning. Lecture Notes In Computer Science 2013, 16: 57-65. PMID: 24505744, DOI: 10.1007/978-3-642-40760-4_8.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsArtificial IntelligenceData Interpretation, StatisticalDogsEchocardiography, Four-DimensionalImage EnhancementImage Interpretation, Computer-AssistedMyocardial InfarctionPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificityStochastic ProcessesSubtraction Technique
2012
Assessment of left ventricular 2D flow pathlines during early diastole using spatial modulation of magnetization with polarity alternating velocity encoding: A study in normal volunteers and canine animals with myocardial infarction
Zhang Z, Friedman D, Dione DP, Lin BA, Duncan JS, Sinusas AJ, Sampath S. Assessment of left ventricular 2D flow pathlines during early diastole using spatial modulation of magnetization with polarity alternating velocity encoding: A study in normal volunteers and canine animals with myocardial infarction. Magnetic Resonance In Medicine 2012, 70: 766-775. PMID: 23044637, PMCID: PMC3844046, DOI: 10.1002/mrm.24517.Peer-Reviewed Original Research
2011
Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor
Pearlman PC, Tagare HD, Lin BA, Sinusas AJ, Duncan JS. Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor. Medical Image Analysis 2011, 16: 351-360. PMID: 22078842, PMCID: PMC3267850, DOI: 10.1016/j.media.2011.09.002.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsComputer SimulationDogsEchocardiography, Three-DimensionalHeart VentriclesImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalModels, CardiovascularModels, StatisticalPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueConceptsLeft ventricular endocardial boundarySpatio-temporal predictorsStandard level setRF dataSpatio-temporal coherenceNeighboring framesImage sequencesBoundary detectionMultiple framesImage inhomogeneitySegmentationEndocardial boundaryGeometric constraintsManual tracingRF ultrasoundAlgorithmLevel setsEchocardiographic imagesFrameConditional modelLinear predictorTrackingSpatial modelImagesRobustnessSegmentation of 3D RF Echocardiography Using a Multiframe Spatio-temporal Predictor
Pearlman PC, Tagare HD, Lin BA, Sinusas AJ, Duncan JS. Segmentation of 3D RF Echocardiography Using a Multiframe Spatio-temporal Predictor. Lecture Notes In Computer Science 2011, 22: 37-48. PMID: 21761644, DOI: 10.1007/978-3-642-22092-0_4.Peer-Reviewed Original ResearchConceptsLeft ventricular endocardial boundarySpatio-temporal predictorsStandard level setSpatio-temporal coherenceNeighboring framesImage sequencesBoundary detectionRF dataMultiple framesImage inhomogeneitySegmentationEndocardial boundaryGeometric constraintsManual tracingRF ultrasoundLevel setsConditional modelEchocardiographic imagesFrameLinear predictorAlgorithmTrackingSpatial modelImagesRobustness
2010
A coupled deformable model for tracking myocardial borders from real-time echocardiography using an incompressibility constraint
Zhu Y, Papademetris X, Sinusas AJ, Duncan JS. A coupled deformable model for tracking myocardial borders from real-time echocardiography using an incompressibility constraint. Medical Image Analysis 2010, 14: 429-448. PMID: 20350833, PMCID: PMC4318707, DOI: 10.1016/j.media.2010.02.005.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsArtificial IntelligenceComputer SystemsDogsEchocardiography, Three-DimensionalElasticity Imaging TechniquesHumansImage EnhancementImage Interpretation, Computer-AssistedPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueConceptsDeformable modelImage-derived informationLV endocardial boundariesImage acquisition techniquesFinal segmentationAutomatic algorithmGround truthManual segmentationVolumetric imagesSegmentationSynthetic dataEndocardial boundaryNumber of effortsMyocardial bordersEpicardial boundariesAcquisition techniquesInstantaneous acquisitionConstraintsImagesEchocardiographic imagesSetSpeckle statisticsAlgorithmReal-time echocardiography3D Radio Frequency Ultrasound Cardiac Segmentation Using a Linear Predictor
Pearlman PC, Tagare HD, Sinusas AJ, Duncan JS. 3D Radio Frequency Ultrasound Cardiac Segmentation Using a Linear Predictor. Lecture Notes In Computer Science 2010, 13: 502-509. PMID: 20879268, PMCID: PMC3889143, DOI: 10.1007/978-3-642-15705-9_61.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsComputer SimulationDogsEchocardiography, Three-DimensionalImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalLinear ModelsModels, CardiovascularMyocardial InfarctionPattern Recognition, AutomatedRadio WavesReproducibility of ResultsSensitivity and SpecificityConceptsLeft ventricular endocardial boundaryStandard level setSpatio-temporal coherenceCardiac segmentationBoundary detectionImage inhomogeneityEndocardial boundarySegmentationGeometric constraintsManual tracingRadio frequency ultrasoundLinear predictorLevel setsRF dataEchocardiographic imagesB-mode dataTrackingImagesDataConstraintsSetDetection
2009
Segmentation of the Left Ventricle from Cardiac MR Images Using a Subject-Specific Dynamical Model
Zhu Y, Papademetris X, Sinusas AJ, Duncan JS. Segmentation of the Left Ventricle from Cardiac MR Images Using a Subject-Specific Dynamical Model. IEEE Transactions On Medical Imaging 2009, 29: 669-687. PMID: 19789107, PMCID: PMC2832728, DOI: 10.1109/tmi.2009.2031063.Peer-Reviewed Original ResearchConceptsSubject-specific dynamical modelGeneric dynamical modelDynamical modelStatistical modelSpecific dynamical modelRecursive Bayesian frameworkDynamic prediction algorithmStatic modelBayesian frameworkCardiac sequenceMotion modelActive Appearance Motion ModelsError propagationSpecific motion patternsPeriodic natureExperimental resultsPropagationCardiac shapeSegmentation resultsBackward directionSequential segmentationDynamicsModelMotion patternsOne-out
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
2005
A Boundary Element-Based Approach to Analysis of LV Deformation
Yan P, Lin N, Sinusas AJ, Duncan JS. A Boundary Element-Based Approach to Analysis of LV Deformation. Lecture Notes In Computer Science 2005, 8: 778-785. PMID: 16685917, DOI: 10.1007/11566465_96.Peer-Reviewed Original Research
2002
Estimation of 3-D Left Ventricular Deformation from Medical Images Using Biomechanical Models
Papademetris* X, Sinusas AJ, Dione DP, Constable RT, Duncan JS. Estimation of 3-D Left Ventricular Deformation from Medical Images Using Biomechanical Models. IEEE Transactions On Medical Imaging 2002, 21: 786. PMID: 12374316, DOI: 10.1109/tmi.2002.801163.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsCoronary DiseaseDogsElasticityFinite Element AnalysisHeart VentriclesHumansImage EnhancementImaging, Three-DimensionalMagnetic Resonance Imaging, CineModels, CardiovascularPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificityStress, MechanicalConceptsDense motion fieldRegional cardiac deformationLinear elastic modelSoft tissue deformationMotion fieldTerms of strainBiomechanical modelDeformation estimationTissue deformationFiber directionDeformationThree-dimensional image sequencesCardiac deformationHeart wallGood agreementHeart deformationGeneric methodologyMuscle fiber directionImage-derived informationImage sequencesEstimationWallSpecific directionQuantitative estimationInitial correspondence
2001
A new method for quantification of spatial and temporal parameters of endocardial motion: evaluation of experimental infarction using magnetic resonance imaging.
Heller EN, Staib LH, Dione DP, Constable RT, Shi CQ, Duncan JS, Sinusas AJ. A new method for quantification of spatial and temporal parameters of endocardial motion: evaluation of experimental infarction using magnetic resonance imaging. Canadian Journal Of Cardiology 2001, 17: 309-18. PMID: 11264564.Peer-Reviewed Original Research
2000
Point-tracked quantitative analysis of left ventricular surface motion from 3-D image sequences
Shi P, Sinusas AJ, Constable RT, Ritman E, Duncan JS. Point-tracked quantitative analysis of left ventricular surface motion from 3-D image sequences. IEEE Transactions On Medical Imaging 2000, 19: 36-50. PMID: 10782617, DOI: 10.1109/42.832958.Peer-Reviewed Original Research