2024
Long-Axial Field-Of-View Limited-Angle PET System with Ultra-High Time-Of-Flight and Depth-Of-Interaction
Marin T, Chemli Y, Razdevšek G, Zhuo Y, Orehar M, Najmaoui Y, Dolenec R, Gascon D, Gola A, Benlloch J, Alamo J, Barbera J, Fernandez-Tenllado J, Gomez S, Korpar S, Krizan P, Majewski S, Manera R, Mariscal-Castilla A, Mauricio J, Merzi S, Morera C, Pavon G, Pavon N, Penna M, Seljak A, Studen A, Zontar D, Pestotnik R, Fakhri G. Long-Axial Field-Of-View Limited-Angle PET System with Ultra-High Time-Of-Flight and Depth-Of-Interaction. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10658297.Peer-Reviewed Original ResearchDepth of interactionDepth-of-interaction resolutionTime-of-flightPET systemImage quality metricsTime-of-flight resolutionField-of-view scanningSacrificing image qualityQuality metricsSilicon photomultipliersDetector panelPET instrumentationImage qualityResolution systemAngle geometryGeometric correctionReduce costsPerformancePhotomultiplierSystemResolutionMetricsElectronPoint-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model
Liu X, Woo J, Ma C, Ouyang J, Fakhri G. Point-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model. 2011 IEEE Nuclear Science Symposium Conference Record 2024, 00: 1-1. PMID: 39445308, PMCID: PMC11497479, DOI: 10.1109/nss/mic/rtsd57108.2024.10656071.Peer-Reviewed Original ResearchSubject-aware PET Denoising with Contrastive Adversarial Domain Generalization
Liu X, Marin T, Eslahi S, Tiss A, Chemli Y, Johson K, Fakhri G, Ouyang J. Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization. 2011 IEEE Nuclear Science Symposium Conference Record 2024, 00: 1-1. PMID: 39445307, PMCID: PMC11497478, DOI: 10.1109/nss/mic/rtsd57108.2024.10656150.Peer-Reviewed Original ResearchDomain generalizationDenoising performanceDenoising moduleDeep learningSubject-independent mannerSubject-invariant featuresSuperior denoising performanceAdversarial learning frameworkSubject-related informationConventional UNetBottleneck featuresTrustworthy systemsLearning frameworkDL modelsDL model performanceDenoisingNoise realizationsNegative samplesList-mode dataImage volumesModel performancePerformancePerformance of positron emission tomographyUNetFraction of eventsA deep learning-based approach to nuisance signal removal from MRSI data aqcuired without suppression
Lee W, Zhuo Y, Marin T, Han P, Chi D, Fakhri G, Ma C. A deep learning-based approach to nuisance signal removal from MRSI data aqcuired without suppression. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/0259.Peer-Reviewed Original ResearchDeep learning-based methodsLearning-based methodsU-Net structureSignal removalIn vivo MRSI dataNeural networkU-NetMRSI dataImage reconstructionSuperior performanceData processingRobust performanceHankel matrixNetworkNuisance signalsConventional methodsPerformanceMRSI signalsSignalMethodRemove nuisance signalsRemovalHankelCross noise level PET denoising with continuous adversarial domain generalization
Liu X, Eslahi S, Marin T, Tiss A, Chemli Y, Huang Y, Johnson K, Fakhri G, Ouyang J. Cross noise level PET denoising with continuous adversarial domain generalization. Physics In Medicine And Biology 2024, 69: 085001. PMID: 38484401, PMCID: PMC11195012, DOI: 10.1088/1361-6560/ad341a.Peer-Reviewed Original ResearchDomain generalization techniqueDomain generalizationDenoising performanceSuperior denoising performanceLatent feature representationGeneral techniqueDistribution shiftsAdversarial trainingDenoised imageFeature representationDomain labelsDistribution divergenceNoise levelDeep learningImage spaceDenoisingPerformance degradationCore ideaNoise realizationsCD methodNoiseImage volumesPerformanceImagesPSNR
2023
Successive Subspace Learning for Cardiac Disease Classification with Two-Phase Deformation Fields from Cine MRI
Liu X, Xing F, Gaggin H, Kuo C, El Fakhri G, Woo J. Successive Subspace Learning for Cardiac Disease Classification with Two-Phase Deformation Fields from Cine MRI. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2023, 00: 1-5. PMID: 38031559, PMCID: PMC10686280, DOI: 10.1109/isbi53787.2023.10230746.Peer-Reviewed Original ResearchTraining samplesCardiovascular disease classificationCNN-based approachDeep learning modelsCardiac disease classificationSubspace learningSSL modelClassification performanceDeep learningCardiac cine magnetic resonance imagingSubspace approximationSupervised regressionLearning modelsAccurate characterization resultsDisease classificationClassificationCardiac atlasLearningDeformation fieldEnd-systolic phaseFrameworkFeedforward designPerformanceTrainingSSLOutlier Robust Disease Classification via Stochastic Confidence Network
Lee K, Lee H, El Fakhri G, Sepulcre J, Liu X, Xing F, Hwang J, Woo J. Outlier Robust Disease Classification via Stochastic Confidence Network. Lecture Notes In Computer Science 2023, 14394: 80-90. DOI: 10.1007/978-3-031-47425-5_8.Peer-Reviewed Original ResearchDeep learningState-of-the-art modelsAccuracy of deep learningState-of-the-artMedical image dataMedical imaging modalitiesImage patchesIrrelevant patchesCategorical featuresPresence of outliersDL modelsConfidence networkConfidence predictionsClassifying outliersData samplesImage dataOutliersExperimental resultsDisease classificationImprove diagnostic performanceClassificationDiagnosing breast tumorsUltrasound imagingPerformanceImages
2022
Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling
Djebra Y, Marin T, Han P, Bloch I, Fakhri G, Ma C. Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling. IEEE Transactions On Medical Imaging 2022, 42: 158-169. PMID: 36121938, PMCID: PMC10024645, DOI: 10.1109/tmi.2022.3207774.Peer-Reviewed Original ResearchConceptsSpace alignmentSampled k-space dataState-of-the-art methodsIntrinsic low-dimensional manifold structureNumerical simulation studyLow-dimensional manifold structureState-of-the-artLinear subspace modelSparsity modelModel-based frameworkSubspace modelManifold structureMathematical modelManifold modelSparse samplingImage reconstructionMRI applicationsDynamic magnetic resonance imagingSpatiotemporal signalsSpatial resolutionPerformanceSimulation studyImagesMethodSparsityAutomation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging
Kong H, Kim J, Moon H, Park H, Kim J, Lim R, Woo J, Fakhri G, Kim D, Kim S. Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging. Scientific Reports 2022, 12: 18118. PMID: 36302815, PMCID: PMC9613909, DOI: 10.1038/s41598-022-22222-z.Peer-Reviewed Original ResearchConceptsSynthetic data augmentationData augmentationLack of training dataConventional data augmentationDeep learning methodsTraining dataLearning methodsPipeline approachAlgorithm trainingGraphical dataAutomationWaters' view radiographsModel performanceAutomated pipelinePerformancePerformance parametersAlgorithmDatasetAugmentationDataMethodPipelineRulesIndustrial workers
2013
Clinical impact of time-of-flight and point response modeling in PET reconstructions: a lesion detection study
Schaefferkoetter J, Casey M, Townsend D, Fakhri G. Clinical impact of time-of-flight and point response modeling in PET reconstructions: a lesion detection study. Physics In Medicine And Biology 2013, 58: 1465-1478. PMID: 23403399, PMCID: PMC3616316, DOI: 10.1088/0031-9155/58/5/1465.Peer-Reviewed Original ResearchConceptsPoint spread functionTime-of-flightBenefit of TOFPET reconstructionNumerical modelOptimal reconstruction parametersLocalization receiver operating characteristicsLesion-detection studiesObserved SNRReconstruction schemeSpread functionReconstruction parametersPerformanceNumerical observationsPatient images