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
2017
High‐resolution dynamic 31P‐MRSI using a low‐rank tensor model
Ma C, Clifford B, Liu Y, Gu Y, Lam F, Yu X, Liang Z. High‐resolution dynamic 31P‐MRSI using a low‐rank tensor model. Magnetic Resonance In Medicine 2017, 78: 419-428. PMID: 28556373, PMCID: PMC5562044, DOI: 10.1002/mrm.26762.Peer-Reviewed Original ResearchConceptsLow-rank tensorImage reconstructionHigh-resolution image reconstructionImage functionSubspace structureData acquisitionFrame-ratePursuit approachCorrelation of dataSubspaceK-space coverageK-spaceImagesSNRMathematical structureReconstructionHigh-resolutionModeling purposesIn vivo studiesMethodTensor
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 experimentsImagesAccelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors
He J, Liu Q, Christodoulou A, Ma C, Lam F, Liang Z. Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors. IEEE Transactions On Medical Imaging 2016, 35: 2119-2129. PMID: 27093543, PMCID: PMC5487008, DOI: 10.1109/tmi.2016.2550204.Peer-Reviewed Original ResearchConceptsLow-rank tensorSparsity constraintImage reconstructionGroup sparsity constraintHigh-dimensional imagesAlternating direction methodCore tensorSubspace estimationData spaceLong data acquisition timeLow-rankUndersampled dataSparse samplingDirection methodData acquisition timeImagesMeasured dataSparsityAcquisition timeConstraintsMathematical structureApplicationsDatasetMRI applicationsSubspace
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 experimentsEncoding and Decoding with Prior Knowledge: From SLIM to SPICE
Ma C, Lam F, Liang Z. Encoding and Decoding with Prior Knowledge: From SLIM to SPICE. 2015, 535-542. DOI: 10.1002/9780470034590.emrstm1441.Peer-Reviewed Original ResearchImage reconstructionLimited-data problemHigh-quality image reconstructionMagnetic resonance spectroscopic imaging methodBoundary informationSparsely sampled dataFourier encodingTruncated Fourier seriesEncodingData acquisitionSpectral localizationConventional magnetic resonance spectroscopic imagingFourier seriesImagesDecodingMagnetic resonance spectroscopic imagingFourierSubspaceSparsenessSpectroscopic imagingCodeDataMethodReconstructionSpices