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 ResearchMeSH KeywordsAlgorithmsComputer SimulationImage Processing, Computer-AssistedMagnetic Resonance ImagingModels, TheoreticalConceptsSpace 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 studyImagesMethodSparsityJoint 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 ResearchMeSH KeywordsAlgorithmsBrainComputer SimulationHumansMagnetic Resonance ImagingMagnetic Resonance SpectroscopyProton Magnetic Resonance SpectroscopyConceptsSubspace-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
2016
High‐resolution 1H‐MRSI of the brain using short‐TE SPICE
Ma C, Lam F, Ning Q, Johnson C, Liang Z. High‐resolution 1H‐MRSI of the brain using short‐TE SPICE. Magnetic Resonance In Medicine 2016, 77: 467-479. PMID: 26841000, PMCID: PMC5493212, DOI: 10.1002/mrm.26130.Peer-Reviewed Original ResearchConceptsSignal-to-noise ratioHigh-resolution spectroscopic imagingSpatiospectral correlationSpectroscopic imagingIn-plane resolutionSubspace-based techniquesAccelerated data acquisitionSignal processing algorithmsMetabolite signalsIn-planeProcessing algorithmsNuisance signalsLipid signalingBaseline signalDatasetData acquisitionProperties of water
2014
Improved Image Reconstruction for Subspace-Based Spectroscopic Imaging Using Non-Quadratic Regularization
Wu Z, Lam F, Ma C, Liang Z. Improved Image Reconstruction for Subspace-Based Spectroscopic Imaging Using Non-Quadratic Regularization. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2014, 2014: 2432-2435. PMID: 25570481, DOI: 10.1109/embc.2014.6944113.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsComputer SimulationImage Processing, Computer-AssistedMagnetic Resonance SpectroscopyPhantoms, ImagingSignal-To-Noise RatioConceptsImage reconstructionLow-rank modelNon-quadratic regularizationHigh-resolution metabolic imagingSparsely sampled datasetsCapabilities of SPICESPICE frameworkOptimization problemPrimal-dualNon-quadraticImagesSNRAlgorithmDatasetPhantom studySparsenessSpectroscopic imaging methodReconstructionSpectroscopic imagingOptimizationRegularizationMethodCapability
2009
Perturbation Analysis of the Effects of $B_{1}^{+}$ Errors on Parallel Excitation in MRI
Ma C, Xu D, King K, Liang Z. Perturbation Analysis of the Effects of $B_{1}^{+}$ Errors on Parallel Excitation in MRI. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2009, 1: 4064-4066. PMID: 19964100, DOI: 10.1109/iembs.2009.5333196.Peer-Reviewed Original Research