2023
Quantitative cardiovascular magnetic resonance findings and clinical risk factors predict cardiovascular outcomes in breast cancer patients
Kwan J, Arbune A, Henry M, Hu R, Wei W, Nguyen V, Lee S, Lopez-Mattei J, Guha A, Huber S, Bader A, Meadows J, Sinusas A, Mojibian H, Peters D, Lustberg M, Hull S, Baldassarre L. Quantitative cardiovascular magnetic resonance findings and clinical risk factors predict cardiovascular outcomes in breast cancer patients. PLOS ONE 2023, 18: e0286364. PMID: 37252927, PMCID: PMC10228774, DOI: 10.1371/journal.pone.0286364.Peer-Reviewed Original ResearchConceptsBreast cancer patientsSystolic heart failureCardiovascular outcomesCancer patientsHeart failureValvular diseaseStrain abnormalitiesLeft ventricular ejection fraction reductionCancer treatment-related cardiotoxicityCardiovascular magnetic resonance findingsVentricular ejection fraction reductionYale-New Haven HospitalEjection fraction reductionTreatment-related cardiotoxicityAdverse cardiovascular outcomesClinical risk factorsNormal LV functionGlobal longitudinal strainIschemic heart diseaseMagnetic resonance findingsRisk regression modelsNew Haven HospitalSubclinical cardiotoxicityDiastolic dysfunctionStatin use
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 ResearchMeSH KeywordsEchocardiographyEchocardiography, Three-DimensionalHeartHumansMyocardial InfarctionVentricular Dysfunction, LeftConceptsSpatial 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
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 ResearchMeSH KeywordsAlgorithmsAnimalsDogsEchocardiographyImage Processing, Computer-AssistedImaging, Three-DimensionalMotionNeural Networks, ComputerVentricular Dysfunction, LeftConceptsDeformation/strainExcellent tracking accuracyEntire cardiac cycleTracking accuracyCardiac motion analysisAccurate estimationSurface pointsEchocardiographic image sequencesLV motionDisplacementMotion analysisImage sequencesCardiac cyclePoint matchingMotionConsecutive framesEstimationNetwork trackingImportant characteristicsSignificant promiseSchemeGood correlationFlow
1998
Physical and geometrical modeling for image-based recovery of left ventricular deformation
Duncan J, Shi P, Constable T, Sinusas A. Physical and geometrical modeling for image-based recovery of left ventricular deformation. Progress In Biophysics And Molecular Biology 1998, 69: 333-351. PMID: 9785945, DOI: 10.1016/s0079-6107(98)00014-5.Peer-Reviewed Original Research