2025
Artificial Intelligence–Guided Lung Ultrasound by Nonexperts
Baloescu C, Bailitz J, Cheema B, Agarwala R, Jankowski M, Eke O, Liu R, Nomura J, Stolz L, Gargani L, Alkan E, Wellman T, Parajuli N, Marra A, Thomas Y, Patel D, Schraft E, O’Brien J, Moore C, Gottlieb M. Artificial Intelligence–Guided Lung Ultrasound by Nonexperts. JAMA Cardiology 2025, 10: 245-253. PMID: 39813064, PMCID: PMC11904735, DOI: 10.1001/jamacardio.2024.4991.Peer-Reviewed Original ResearchThis study shows AI helps non-experts create expert-quality lung ultrasound images, which may improve healthcare diagnostics access in underserved areas.Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy
Guo M, Wu Y, Hobson C, Su Y, Qian S, Krueger E, Christensen R, Kroeschell G, Bui J, Chaw M, Zhang L, Liu J, Hou X, Han X, Lu Z, Ma X, Zhovmer A, Combs C, Moyle M, Yemini E, Liu H, Liu Z, Benedetto A, La Riviere P, Colón-Ramos D, Shroff H. Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy. Nature Communications 2025, 16: 313. PMID: 39747824, PMCID: PMC11697233, DOI: 10.1038/s41467-024-55267-x.Peer-Reviewed Original ResearchConceptsAdaptive optics techniquesMulti-photonDeep learning-based strategyAberration compensationLearning-based strategyTrained neural networkImprove image qualityOptical aberrationsNeural networkImage quantitationOptical techniquesDiverse datasetsSuper-resolution microscopyLight sheetRestore dataImage qualityImage signalNetworkImage inspectionImage acquisitionImage stacksOpticsResolutionImagesFluorescence microscopy
2024
Adaptive 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
2023
Multi-task Learning for Hierarchically-Structured Images: Study on Echocardiogram View Classification
Charton J, Ren H, Kim S, Gonzalez C, Khambhati J, Cheng J, DeFrancesco J, Waheed A, Marciniak S, Moura F, Cardoso R, Lima B, Picard M, Li X, Li Q. Multi-task Learning for Hierarchically-Structured Images: Study on Echocardiogram View Classification. Lecture Notes In Computer Science 2023, 14337: 185-194. DOI: 10.1007/978-3-031-44521-7_18.Peer-Reviewed Original ResearchDeep learningMulti-task learning schemeVideo classification tasksMulti-task learningImage classification scenariosResidual neural networkData labelsClassification taskLearning schemeAblation studiesClassification scenariosNeural networkTree structureMedical imagesImage-processing techniquesSuperior performanceVideo viewingMonitoring cardiovascular diseasesTraining methodsHierarchically-structuredImage acquisitionLearningHierarchical structureTaskClassificationMagnetic resonance imaging using a nonuniform Bo (NuBo) field-cycling magnet
Selvaganesan K, Wan Y, Ha Y, Wu B, Hancock K, Galiana G, Constable R. Magnetic resonance imaging using a nonuniform Bo (NuBo) field-cycling magnet. PLOS ONE 2023, 18: e0287344. PMID: 37319289, PMCID: PMC10270621, DOI: 10.1371/journal.pone.0287344.Peer-Reviewed Original ResearchConceptsInnovative data acquisitionMain magnetGradient coilsExperimental verificationEcho signalsInitial designField cyclingParallel imagingSuperior soft tissue contrastMagnetsInhomogeneous fieldsLow fieldsSoft tissue contrastReconstruction approachInhomogeneity effectsData acquisitionSpatial encodingPolarization phaseOpen MR systemPowerful noninvasive diagnostic toolSpin echo signalImage acquisitionFieldCoilNew approachMulti-Task Deep Learning and Uncertainty Estimation for Pet Head Motion Correction
Lieffrig E, Zeng T, Zhang J, Fontaine K, Fang X, Revilla E, Lu Y, Onofrey J. Multi-Task Deep Learning and Uncertainty Estimation for Pet Head Motion Correction. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2023, 00: 1-5. PMID: 38111738, PMCID: PMC10725741, DOI: 10.1109/isbi53787.2023.10230791.Peer-Reviewed Original ResearchMulti-task deep learningMulti-task architectureMonte Carlo dropoutTesting subjectsDeep learningMotion tracking deviceSupervised learningMotion correction methodNetwork predictionHead motion correctionAppearance predictionReconstructed imagesPrediction performanceImage acquisitionImage qualityTracking deviceMotion correctionLearning processUncertainty estimationTomography image acquisitionHead motionPrediction uncertaintyLearningQualitative resultsArchitectureThree-Dimensional Printing of the Patellofemoral Joints of Patellar Instability Patients
Beitler B, Yu K, Wang A, Frumberg D, Tommasini S, Wiznia D, Cooperman D, Lattanza L, Fulkerson J. Three-Dimensional Printing of the Patellofemoral Joints of Patellar Instability Patients. Arthroscopy Techniques 2023, 12: e401-e406. PMID: 37013007, PMCID: PMC10066413, DOI: 10.1016/j.eats.2022.11.023.Peer-Reviewed Original ResearchAAPM Task Group Report 238: 3D C‐arms with volumetric imaging capability*
Supanich M, Siewerdsen J, Fahrig R, Farahani K, Gang G, Helm P, Jans J, Jones K, Koenig T, Kuhls-Gilcrist A, Lin M, Riddell C, Ritschl L, Schafer S, Schueler B, Silver M, Timmer J, Trousset Y, Zhang J. AAPM Task Group Report 238: 3D C‐arms with volumetric imaging capability*. Medical Physics 2023, 50: e904-e945. PMID: 36710257, PMCID: PMC11584023, DOI: 10.1002/mp.16245.Peer-Reviewed Original ResearchConceptsImage qualityGeometric calibrationSystem calibrationPhantom imagesService callsC-arm systemImage reconstructionVolumetric imaging capabilityImage acquisitionGeometric alignmentImage-guided radiation therapyData setsTesting approachCBCT systemC-arm cone beamC-armReconstruction characteristicsImage artifactsValuable dataC-arm CBCT systemImagesIGRT systemTask groupDifferent systemsCT image qualityQuantifying velopharyngeal motion variation in speech sound production using an audio-informed dynamic MRI atlas
Xing F, Jin R, Gilbert I, El Fakhri G, Perry J, Sutton B, Woo J. Quantifying velopharyngeal motion variation in speech sound production using an audio-informed dynamic MRI atlas. Proceedings Of SPIE--the International Society For Optical Engineering 2023, 12464: 124642m-124642m-6. PMID: 37621417, PMCID: PMC10448831, DOI: 10.1117/12.2654082.Peer-Reviewed Original ResearchMotion fieldReal-time speechHigh-dimensional datasetsAudio waveformAtlas spaceTemporal alignmentMotion variationsDatasetMagnetic resonance imagingMotion atlasMotion differencesSpeech variationImage acquisitionTaskSpeechMotion characteristicsDynamic magnetic resonance imagingPrincipal componentsImages
2021
Use of Artificial Intelligence in Nononcologic Interventional Radiology: Current State and Future Directions
Malpani R, Petty C, Bhatt N, Staib L, Chapiro J. Use of Artificial Intelligence in Nononcologic Interventional Radiology: Current State and Future Directions. Digestive Disease Interventions 2021, 05: 331-337. PMID: 35005333, PMCID: PMC8740955, DOI: 10.1055/s-0041-1726300.Peer-Reviewed Original ResearchArtificial intelligenceApplication of AINatural language processingSoftware interactionsAI researchLanguage processingExponential advancementDevice navigationImage acquisitionBlack boxIntelligenceFuture of radiologyCurrent stateFuture directionsRoboticsApplicationsNavigationExecutionOutcome predictionResearch LaboratoryRadiomicsProcessingProcedural planningPlanningRadiology
2018
Wide dynamic range high-speed three-dimensional quantitative OCT angiography with a hybrid-beam scan.
Park T, Jang S, Han M, Ryu S, Oh W. Wide dynamic range high-speed three-dimensional quantitative OCT angiography with a hybrid-beam scan. Optics Letters 2018, 43: 2237-2240. PMID: 29762561, DOI: 10.1364/ol.43.002237.Peer-Reviewed Original Research
2016
Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation
Zhang F, Kanik J, Mansi T, Voigt I, Sharma P, Ionasec RI, Subrahmanyan L, Lin BA, Sugeng L, Yuh D, Comaniciu D, Duncan J. Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation. Medical Image Analysis 2016, 35: 599-609. PMID: 27718462, DOI: 10.1016/j.media.2016.09.006.Peer-Reviewed Original ResearchConceptsMitral valve modelingTemporal informationPatient-specific modelingImage acquisitionEuclidean distanceValve modelingComputational frameworkExtended Kalman filterImage analysisModeling frameworkKalman filterFrameworkAverage errorMitral valve geometryTEE imagesInformationMachineParameter estimationClosed mitral valveLeaflet material propertiesSubjective predictionModelingImagesRepresentationOptimizationThree-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation
Prabhu D, Mehanna E, Gargesha M, Brandt E, Wen D, van Ditzhuijzen N, Chamie D, Yamamoto H, Fujino Y, Alian A, Patel J, Costa M, Bezerra H, Wilson D. Three-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation. Journal Of Medical Imaging 2016, 3: 026004-026004. PMID: 27429997, PMCID: PMC4923671, DOI: 10.1117/1.jmi.3.2.026004.Peer-Reviewed Original ResearchCryo-image volumesRegistration errorRegistration methodAutomated plaque characterizationThree-dimensional registrationRegistration accuracyIVOCT image framesHigh-contrastOptical coherence tomographyPerpendicular imagesIntravascular optical coherence tomographySynthetic phantomsCoherence tomographyHigh-resolutionRegistration modelReference framePhantomImage acquisitionThree-dimensionalIntravascular imaging modalitiesImaging modalitiesCatheter parametersRegistrationFrame intervalImage frames3D registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation
Prabhu D, Mehanna E, Gargesha M, Wen D, Brandt E, van Ditzhuijzen N, Chamie D, Yamamoto H, Fujino Y, Farmazilian A, Patel J, Costa M, Bezerra H, Wilson D. 3D registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation. Proceedings Of SPIE--the International Society For Optical Engineering 2016, 9788: 97882c-97882c-9. PMID: 27162417, PMCID: PMC4859892, DOI: 10.1117/12.2217537.Peer-Reviewed Original ResearchCryo-image volumesDigital phantom casesPhantom caseOptical coherence tomographyAutomated plaque characterizationRegistration errorRegistration accuracyCoherence tomographyIntravascular optical coherence tomographyPerpendicular imagesRegistration methodBland-Altman analysisHigh-resolutionDice coefficientIntravascular imaging modalitiesCoronary arteryReference frameImaging modalitiesPlaque featuresCatheter parametersLocal minimaImage acquisitionPlaque typePlaque characterizationCatheter
2012
Automatic Detection of Subcellular Particles in Fluorescence Microscopy via Feature Clustering and Bayesian Analysis*
Liang L, Xu Y, Shen H, De Camilli P, Toomre D, Duncan J. Automatic Detection of Subcellular Particles in Fluorescence Microscopy via Feature Clustering and Bayesian Analysis*. 2012, 1: 161-166. DOI: 10.1109/mmbia.2012.6164750.Peer-Reviewed Original Research
2010
Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery
DeLorenzo C, Papademetris X, Staib LH, Vives KP, Spencer DD, Duncan JS. Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery. IEEE Transactions On Medical Imaging 2010, 29: 322-338. PMID: 20129844, PMCID: PMC2824434, DOI: 10.1109/tmi.2009.2027993.Peer-Reviewed Original ResearchConceptsDeformation estimationSurface deformationBrain surface deformationSurface deformation estimationPreoperative brain imagesCortical surface deformationSurface trackingCamera calibration parametersDisplacement errorStereo vision systemBrain deformationDeformationCalibration parametersBiomechanical modelIntraoperative brainCalibration errorsPhysical processesVision systemVivo casesCamera calibrationStereo systemInitial conditionsImage acquisitionErrorEstimation
2008
Overview of Computer-Assisted Image-Guided Surgery of the Spine
Patel A, Whang P, Vaccaro A. Overview of Computer-Assisted Image-Guided Surgery of the Spine. Seminars In Spine Surgery 2008, 20: 186-194. DOI: 10.1053/j.semss.2008.06.005.Peer-Reviewed Original Research
2007
Evaluation of alterations on mitral annulus velocities, strain, and strain rates due to abrupt changes in preload elicited by parabolic flight
Caiani E, Weinert L, Takeuchi M, Veronesi F, Sugeng L, Corsi C, Capderou A, Cerutti S, Vaïda P, Lang R. Evaluation of alterations on mitral annulus velocities, strain, and strain rates due to abrupt changes in preload elicited by parabolic flight. Journal Of Applied Physiology 2007, 103: 80-87. PMID: 17615285, DOI: 10.1152/japplphysiol.00625.2006.Peer-Reviewed Original ResearchMeSH KeywordsAdaptation, PhysiologicalAdultEchocardiography, DopplerEchocardiography, Doppler, ColorFeasibility StudiesHeart RateHeart SeptumHeart VentriclesHumansHypergravityImage Interpretation, Computer-AssistedLower Body Negative PressureMaleMiddle AgedMitral ValveMyocardial ContractionReproducibility of ResultsResearch DesignSpace FlightStress, MechanicalVentricular Function, LeftWeightlessness SimulationConceptsDoppler tissue echocardiographyLower body negative pressureNormal subjectsPreload dependenceTissue velocityMitral annulus velocityBasal interventricular septumMmHg lower body negative pressurePeak systolic strainMyocardial velocity curvesS', EPreload modificationStanding upright positionAnnulus velocitySystolic strainRegional myocardial functionInterventricular septumEvaluation of alterationsMyocardial functionAnimal modelsPeak systolicMyocardial propertiesUpright positionImage acquisitionReversible mannerNonrigid Intraoperative Cortical Surface Tracking Using Game Theory
DeLorenzo C, Papademetris X, Staib L, Vives K, Spencer D, Duncan J. Nonrigid Intraoperative Cortical Surface Tracking Using Game Theory. 2015 IEEE International Conference On Computer Vision (ICCV) 2007, 1-8. DOI: 10.1109/iccv.2007.4409135.Peer-Reviewed Original ResearchSurface deformationPreoperative brain imagesCortical surface deformationSurface trackingDeformation estimationStereo vision systemBrain deformationDeformationBiomechanical modelIntraoperative brainCalibration errorsPhysical processesVision systemCamera calibrationStereo systemImage acquisitionUsing Game TheoryGame theory
2005
Overcoming the limitations of integrated clinical digital imaging solutions.
Sinard JH, Mattie ME. Overcoming the limitations of integrated clinical digital imaging solutions. Archives Of Pathology & Laboratory Medicine 2005, 129: 1118-26. PMID: 16119983, DOI: 10.5858/2005-129-1118-otloic.Peer-Reviewed Original ResearchConceptsScalable solutionImaging solutionDigital imagingWork flowImage acquisition stepDigital image acquisitionAnatomic pathology departmentArchiving processGreater usabilityMultiuser environmentNovel solutionAcquisition stepImage acquisitionNumber of photographsSoftwareAdditional flexibilityNumber of advantagesUsabilityMinimal trainingVendorsDeploymentSolutionAdvantagesWidespread acceptanceStorage
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