2024
Subject-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 eventsCross 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
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 studyImagesMethodSparsity
2013
Numerical Surrogates for Human Observers in Myocardial Motion Evaluation From SPECT Images
Marin T, Kalayeh M, Parages F, Brankov J. Numerical Surrogates for Human Observers in Myocardial Motion Evaluation From SPECT Images. IEEE Transactions On Medical Imaging 2013, 33: 38-47. PMID: 23981533, PMCID: PMC4148467, DOI: 10.1109/tmi.2013.2279517.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBiomimeticsCoronary Artery DiseaseExpert SystemsHumansImage EnhancementImage Interpretation, Computer-AssistedMotionMyocardial Perfusion ImagingObserver VariationReproducibility of ResultsSensitivity and SpecificitySupport Vector MachineTomography, Emission-Computed, Single-PhotonConceptsHuman observer performanceRelevance vector machinePredicting human observer performanceDeformable mesh modelHuman observersHuman observer scoresImage-quality assessmentPerfusion-defect detectionMotion estimationModel observerMotion featuresVector machineMedical imagesLinear discriminantDefect detectionDetection taskMesh modelObserver performanceDiagnostic tasksNumerical surrogateTask-based approachApproximate surrogateTaskMotion evaluationPerformance