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 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 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
2016
High‐resolution 1H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction
Lam F, Ma C, Clifford B, Johnson C, Liang Z. High‐resolution 1H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction. Magnetic Resonance In Medicine 2016, 76: spcone-spcone. DOI: 10.1002/mrm.26460.Peer-Reviewed Original ResearchImage reconstructionSubspace structureSpectroscopic imaging sequenceSubspace modelImage sequencesEdge-preserving regularizationReconstruction methodThrough-plane resolutionData acquisitionImage reconstruction methodIn-planeIn vivo brain experimentsEncoding schemeField inhomogeneity correctionIn-plane resolutionTwo-dimensional (2DImaging frameworkInhomogeneity correctionData setsSubspaceHybrid data setsSpectroscopic imagingSpatial resolutionBrain experimentsImages
2015
High‐resolution 1H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction
Lam F, Ma C, Clifford B, Johnson C, Liang Z. High‐resolution 1H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction. Magnetic Resonance In Medicine 2015, 76: 1059-1070. PMID: 26509928, PMCID: PMC4848237, DOI: 10.1002/mrm.26019.Peer-Reviewed Original ResearchConceptsSubspace structureSpectroscopic imaging sequenceImage reconstructionSubspace modelImage sequencesImage reconstruction purposesEdge-preserving regularizationData acquisitionReconstruction methodThrough-plane resolutionImage reconstruction methodIn-planeIn vivo brain experimentsEncoding schemeField inhomogeneity correctionIn-plane resolutionTwo-dimensional (2DImaging frameworkInhomogeneity correctionData setsSubspaceHigh-resolutionHybrid data setsSpatial resolutionBrain experiments