2024
Accelerated 3D metabolite T1 mapping of the brain using variable‐flip‐angle SPICE
Zhao Y, Li Y, Guo R, Jin W, Sutton B, Ma C, Fakhri G, Li Y, Luo J, Liang Z. Accelerated 3D metabolite T1 mapping of the brain using variable‐flip‐angle SPICE. Magnetic Resonance In Medicine 2024, 92: 1310-1322. PMID: 38923032, DOI: 10.1002/mrm.30200.Peer-Reviewed Original ResearchConceptsLow-rank tensor modelGeneralized series modelMetabolite TExperimental resultsBrain metabolitesClinically acceptable scan timeEfficient encodingPhantom experimental resultsAcceptable scan timeNoisy dataSparse samplingImaging problemsData processingHealthy subject dataVariable flip angleFlip angleTensor modelSaturation effectsQuantitative metabolic imagingMRSI techniquePhantomScan timeData acquisitionMetabolic imagingT1 mapping
2023
B1 inhomogeneity‐corrected T1 mapping and quantitative magnetization transfer imaging via simultaneously estimating Bloch‐Siegert shift and magnetization transfer effects
Jang A, Han P, Ma C, Fakhri G, Wang N, Samsonov A, Liu F. B1 inhomogeneity‐corrected T1 mapping and quantitative magnetization transfer imaging via simultaneously estimating Bloch‐Siegert shift and magnetization transfer effects. Magnetic Resonance In Medicine 2023, 90: 1859-1873. PMID: 37427533, PMCID: PMC10528411, DOI: 10.1002/mrm.29778.Peer-Reviewed Original ResearchConceptsBloch-Siegert shiftBloch-SiegertMagnetization transfer effectsMonte Carlo simulationsSpin-lattice relaxationSpin-bath modelMagnetization transferBinary spin-bath modelCarlo simulationsProton fractionOff-resonance irradiationIn vivo brain studiesBloch simulationsPhantom experimentsMagnetizationEstimationTransmitted fieldQuantitative magnetization transferMethod performanceMT effectSignal equation
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 data
2021
High-Resolution Label-Free Molecular Imaging of Brain Tumor
Guo R, Ma C, Li Y, Zhao Y, Wang T, Li Y, Fakhri G, Liang Z. High-Resolution Label-Free Molecular Imaging of Brain Tumor. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2021, 00: 3049-3052. PMID: 34891886, DOI: 10.1109/embc46164.2021.9630623.Peer-Reviewed Original ResearchConceptsMagnetic resonance spectroscopic imagingBrain tumorsIntra-tumoural metabolic heterogeneityAssessment of treatment efficacySmall-sized tumorsClinical applicationN-acetyl aspartateBrain tumor characterizationPotential clinical applicationsApplication of magnetic resonance spectroscopic imagingTumor characterizationMolecular imaging techniquesBrain metabolitesImaging of brain tumorsTumorTreatment efficacyClinical relevanceMetabolic imagingDiagnosed brain tumorsHigh-resolution metabolic imagingMetabolic heterogeneityTumor detectionMolecular imaging of brain tumorsLabel-free molecular imagingLabel-free molecular imaging technique
2020
Accelerated J‐resolved 1H‐MRSI with limited and sparse sampling of (‐space
Tang L, Zhao Y, Li Y, Guo R, Clifford B, Fakhri G, Ma C, Liang Z, Luo J. Accelerated J‐resolved 1H‐MRSI with limited and sparse sampling of (‐space. Magnetic Resonance In Medicine 2020, 85: 30-41. PMID: 32726510, PMCID: PMC7992196, DOI: 10.1002/mrm.28413.Peer-Reviewed Original Research
2019
MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac 18F-FDG imaging
Petibon Y, Sun T, Han P, Ma C, Fakhri G, Ouyang J. MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac 18F-FDG imaging. Physics In Medicine And Biology 2019, 64: 195009. PMID: 31394518, PMCID: PMC7007962, DOI: 10.1088/1361-6560/ab39c2.Peer-Reviewed Original ResearchConceptsMR-based motion correctionRespiratory motion correctionMotion correctionImproved spatial resolutionReconstructed activity concentrationCardiac PET dataSpatial resolutionCoincidence eventsMR-basedPET imagingContrast-to-noise ratioCardiac PET imagingRespiratory phasesMC dataImprove image qualityMR acquisitionQuantitative accuracyCardiac PETPET dataActivity concentrationsMyocardium wallF-FDG PETDynamics studiesImage qualityMotion artifactsFree-Breathing Three-Dimensional T1 Mapping of the Heart Using Subspace-Based Data Acquisition and Image Reconstruction
Han P, Horng D, Marin T, Petibon Y, Ouyang J, Fakhri G, Ma C. Free-Breathing Three-Dimensional T1 Mapping of the Heart Using Subspace-Based Data Acquisition and Image Reconstruction. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2019, 00: 4008-4011. PMID: 31946750, DOI: 10.1109/embc.2019.8856511.Peer-Reviewed Original ResearchConceptsRespiratory motionRespiratory gatingLongitudinal relaxation timeSubspace-based methodsLow-rank tensorMagnetic resonance imagingRelaxation timeT1 mappingT)-spaceSubspace-basedSparsity constraintDynamic MR imagingReconstructed mapsSpatiotemporal correlationThree-dimensionalCardiac MRHealthy subjectsIn vivo dataMagnetizationResonance imagingImage functionMR imagingData acquisitionClinical applicationTensor
2018
SERIAL transmit – parallel receive (STxPRx) MR imaging produces acceptable proton image uniformity without compromising field of view or SAR guidelines for human neuroimaging at 9.4 Tesla
Thulborn K, Ma C, Sun C, Atkinson I, Claiborne T, Umathum R, Wright S, Liang Z. SERIAL transmit – parallel receive (STxPRx) MR imaging produces acceptable proton image uniformity without compromising field of view or SAR guidelines for human neuroimaging at 9.4 Tesla. Journal Of Magnetic Resonance 2018, 293: 145-153. PMID: 30012280, PMCID: PMC6084804, DOI: 10.1016/j.jmr.2018.05.009.Peer-Reviewed Original ResearchConceptsProton MR imagesMR images of human brainArrays of surface coilsImages of human brainSurface coil arrayHuman brain imagingImage uniformityField of viewExcitation profilesSAR guidelinesBirdcage coilFLASH pulse sequenceSensitivity correctionSodium MR imagingPulse sequenceAcceptable uniformityCoil arrayExcitationSignal-to-noiseProtonHigh-resolution imagesSurface coilParallel receiverCoilMR imaging
2017
A minimum-phase Shinnar-Le Roux spectral-spatial excitation RF pulse for simultaneous water and lipid suppression in 1H-MRSI of body extremities
Han P, Ma C, Deng K, Hu S, Jee K, Ying K, Chen Y, Fakhri G. A minimum-phase Shinnar-Le Roux spectral-spatial excitation RF pulse for simultaneous water and lipid suppression in 1H-MRSI of body extremities. Magnetic Resonance Imaging 2017, 45: 18-25. PMID: 28917812, PMCID: PMC5709164, DOI: 10.1016/j.mri.2017.09.008.Peer-Reviewed Original ResearchHigh‐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
Spectral Quantification for High-Resolution MR Spectroscopic Imaging With Spatiospectral Constraints
Ning Q, Ma C, Lam F, Liang Z. Spectral Quantification for High-Resolution MR Spectroscopic Imaging With Spatiospectral Constraints. IEEE Transactions On Biomedical Engineering 2016, 64: 1178-1186. PMID: 27479954, PMCID: PMC5513734, DOI: 10.1109/tbme.2016.2594583.Peer-Reviewed Original ResearchAccelerated 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 applicationsSubspaceHigh‐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
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 experimentsImproved Low-Rank Filtering of Magnetic Resonance Spectroscopic Imaging Data Corrupted by Noise and <inline-formula><tex-math notation="LaTeX">$B_0$</tex-math></inline-formula> Field Inhomogeneity
Liu Y, Ma C, Clifford B, Lam F, Johnson C, Liang Z. Improved Low-Rank Filtering of Magnetic Resonance Spectroscopic Imaging Data Corrupted by Noise and$B_0$ Peer-Reviewed Original ResearchConceptsLow-rank filterSignal-to-noise ratioConstrained Cramer-RaoDenoising MRSI dataFiltering methodLow-rank modelCramer-RaoDenoising performanceRank minimizationHigh-resolution magnetic resonance spectroscopic imagingBoundary constraintsIn vivo MRSI dataData corruptionLow-rankMRSI dataFilterNoiseUpper boundB0 field inhomogeneityField inhomogeneity correctionDenoisingField inhomogeneityInhomogeneity correctionMethodMagnetic resonance spectroscopic imagingRemoval of nuisance signals from limited and sparse 1H MRSI data using a union‐of‐subspaces model
Ma C, Lam F, Johnson C, Liang Z. Removal of nuisance signals from limited and sparse 1H MRSI data using a union‐of‐subspaces model. Magnetic Resonance In Medicine 2015, 75: 488-497. PMID: 25762370, PMCID: PMC4567537, DOI: 10.1002/mrm.25635.Peer-Reviewed Original Research
2014
Design of multidimensional Shinnar–Le Roux radiofrequency pulses
Ma C, Liang Z. Design of multidimensional Shinnar–Le Roux radiofrequency pulses. Magnetic Resonance In Medicine 2014, 73: 633-645. PMID: 24578212, PMCID: PMC4147023, DOI: 10.1002/mrm.25179.Peer-Reviewed Original ResearchField-Inhomogeneity-Corrected Low-Rank Filtering of Magnetic Resonance Spectroscopic Imaging Data
Liu Y, Ma C, Clifford B, Lam F, Johnson C, Liang Z. Field-Inhomogeneity-Corrected Low-Rank Filtering of Magnetic Resonance Spectroscopic Imaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2014, 2014: 6422-6425. PMID: 25571466, DOI: 10.1109/embc.2014.6945098.Peer-Reviewed Original ResearchImproved 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 ResearchConceptsImage reconstructionLow-rank modelNon-quadratic regularizationHigh-resolution metabolic imagingSparsely sampled datasetsCapabilities of SPICESPICE frameworkOptimization problemPrimal-dualNon-quadraticImagesSNRAlgorithmDatasetPhantom studySparsenessSpectroscopic imaging methodReconstructionSpectroscopic imagingOptimizationRegularizationMethodCapability