2022
Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment‐based manifold learning
Ma C, Han P, Zhuo Y, Djebra Y, Marin T, Fakhri G. Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment‐based manifold learning. Magnetic Resonance In Medicine 2022, 89: 1297-1313. PMID: 36404676, PMCID: PMC9892363, DOI: 10.1002/mrm.29526.Peer-Reviewed Original ResearchConceptsSubspace-based methodsManifold learningIntrinsic low-dimensional structureGlobal coordinationLearning-based methodsNumerical simulation dataSpatial smoothness constraintSparsity constraintSpace alignmentSubspace modelSmoothness constraintSuperior performanceRoot mean square errorLinear transformationMechanical simulationsLow-dimensionalSquare errorSubspaceExperimental dataSpectroscopic imagingQuantum mechanical simulationsCoordinate alignmentMR spectroscopic imagingSpectral quantificationSimulated dataMRSI spectral quantification using linear tangent space alignment (LTSA)-based manifold learning
Ma C, Fakhri G. MRSI spectral quantification using linear tangent space alignment (LTSA)-based manifold learning. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2022 DOI: 10.58530/2022/0243.Peer-Reviewed Original Research
2015
Matched signal detection on graphs: Theory and application to brain imaging data classification
Hu C, Sepulcre J, Johnson K, Fakhri G, Lu Y, Li Q. Matched signal detection on graphs: Theory and application to brain imaging data classification. NeuroImage 2015, 125: 587-600. PMID: 26481679, DOI: 10.1016/j.neuroimage.2015.10.026.Peer-Reviewed Original ResearchConceptsImage data classificationWeighted energy detectorGraph-signalGraph Laplacian eigenvaluesEnergy detectorManifold structureProblem of Alzheimer's diseaseData classificationGraph LaplacianSubspace detectorWeighted graphMSD approachSignal processingSignal detectionIntrinsic structureLaplacian eigenvaluesSubspaceTest statisticsGraphRandom signalsData setsLowest eigenvalueGaussian distributionTraditional methodsEigenvalues
2013
Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification
Hu C, Cheng L, Sepulcre J, El Fakhri G, Lu Y, Li Q. Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification. Lecture Notes In Computer Science 2013, 23: 1-12. PMID: 24683953, DOI: 10.1007/978-3-642-38868-2_1.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAlzheimer DiseaseAniline CompoundsBenzothiazolesBrainBrain MappingConnectomeHumansImage EnhancementImage Interpretation, Computer-AssistedNerve NetNeural PathwaysPattern Recognition, AutomatedPositron-Emission TomographyReproducibility of ResultsSensitivity and SpecificityThiazolesTissue DistributionConceptsBrain network classificationNetwork classification problemWeighted energy detectorPrinciple component analysisSub-manifold structureTraditional principle component analysisSubspace detectionTraining dataEnergy detectorGraph structureProblem of Alzheimer's diseaseGraph LaplacianNetwork classificationNoise varianceLevel of smoothnessWeighted graphSignal detectionIntrinsic structureSignal modelGraphSubspaceIsing modelNoiseSignal variationsComponent analysis