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 data
2019
EMnet: an unrolled deep neural network for PET image reconstruction
Gong K, Wu D, Kim K, Yang J, Fakhri G, Seo Y, Li Q. EMnet: an unrolled deep neural network for PET image reconstruction. Progress In Biomedical Optics And Imaging 2019, 10948: 1094853-1094853-6. DOI: 10.1117/12.2513096.Peer-Reviewed Original ResearchDeep neural networksPET image reconstructionNeural networkExpectation maximizationImage reconstructionImage denoising applicationNeural network frameworkNeural network denoisersDenoising applicationsDenoising methodNetwork denoisingNetwork trainingNetwork frameworkWhole graphUpdate stepData consistencyIll-posedNetworkInverse problemEMNETDenoisingSimulated dataFrameworkAlgorithmGraph
2015
A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease
Hu C, Cheng L, Sepulcre J, Johnson K, Fakhri G, Lu Y, Li Q. A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease. PLOS ONE 2015, 10: e0128136. PMID: 26024224, PMCID: PMC4449104, DOI: 10.1371/journal.pone.0128136.Peer-Reviewed Original ResearchConceptsNetwork featuresAlzheimer's diseaseConsistent with known pathologyUnknown graphConnection weightsReconstruction networkCortical hubsDegree statisticsData modelSmooth signalsFeatures of brain pathologyOptimization frameworkAmyloid-bPartial correlation estimationImage dataNetworkGraphGlobal connectivity measuresPositron emission tomographyConnectivity measuresNeurodegenerative diseasesConnectivity patternsSample correlationClinical ADSimulated data
2013
A GRAPH THEORETICAL REGRESSION MODEL FOR BRAIN CONNECTIVITY LEARNING OF ALZHEIMER'S DISEASE
Hu C, Cheng L, Sepulcre J, Fakhri G, Lu Y, Li Q. A GRAPH THEORETICAL REGRESSION MODEL FOR BRAIN CONNECTIVITY LEARNING OF ALZHEIMER'S DISEASE. 2013, 616-619. DOI: 10.1109/isbi.2013.6556550.Peer-Reviewed Original Research