2022
Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction
Zhou B, Schlemper J, Dey N, Mohseni Salehi SS, Sheth K, Liu C, Duncan JS, Sofka M. Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction. Medical Image Analysis 2022, 81: 102538. PMID: 35926336, DOI: 10.1016/j.media.2022.102538.Peer-Reviewed Original ResearchConceptsNon-Cartesian MRI reconstructionMRI reconstructionUndersampled dataPrevious baseline methodsSelf-supervised approachSelf-supervised learningHigh-quality reconstructionReconstruction networkAppearance consistencyDataset demonstrateBaseline methodsImage domainDisjoint partitionsSupervised trainingPractical adoptionReconstruction accuracyDomain partitionImproved image qualityImage qualityDDSSSampling patternK-spaceExperimental resultsNetworkMotion robustness
2021
MDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET
Zhou B, Tsai YJ, Chen X, Duncan JS, Liu C. MDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET. IEEE Transactions On Medical Imaging 2021, 40: 3154-3164. PMID: 33909561, PMCID: PMC8588635, DOI: 10.1109/tmi.2021.3076191.Peer-Reviewed Original ResearchConceptsMotion estimationPyramid networkAdversarial networkAccurate motion estimationMotion correctionLow-noise reconstructionGated positron emission tomographyMotion correction methodMotion estimation networkGated PET dataEstimation networkRecurrent layersDenoising NetworkRespiratory motion blurringExperimental resultsLow-noise imagesMotion blurringNoise levelCorrection methodNetworkPET reconstructionPrevious methodsImage qualityImagesEstimationLearning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation
Lu A, Ahn SS, Ta K, Parajuli N, Stendahl JC, Liu Z, Boutagy NE, Jeng GS, Staib LH, O’Donnell M, Sinusas AJ, Duncan JS. Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation. IEEE Transactions On Medical Imaging 2021, 40: 2233-2245. PMID: 33872145, PMCID: PMC8442959, DOI: 10.1109/tmi.2021.3074033.Peer-Reviewed Original Research
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
2008
Motion tracking of the outer tips of microtubules
Hadjidemetriou S, Toomre D, Duncan J. Motion tracking of the outer tips of microtubules. Medical Image Analysis 2008, 12: 689-702. PMID: 18571462, DOI: 10.1016/j.media.2008.04.004.Peer-Reviewed Original ResearchConceptsMicrotubule assemblyCoordination of mitosisTransport of chromosomesHigh throughput quantitative studiesNumerous critical rolesMicrotubule tipsLiving cellsCell migrationStructural tracksConfocal microscopyEpifluorescent microscopyNeurodegenerative diseasesCritical roleCell pathologyMicrotubulesAssemblyCellsSequenceOuter tipChromosomesOrganellesMitosisAbnormal functionCytoplasmVesicles