2024
Sparsity Constrained Linear Tangent Space Alignment Model (LTSA) for 3d Cardiac Extracellular Volume Mapping
Mounime I, Lee W, Marin T, Han P, Djebra Y, Eslahi S, Gori P, Angelini E, Fakhri G, Ma C. Sparsity Constrained Linear Tangent Space Alignment Model (LTSA) for 3d Cardiac Extracellular Volume Mapping. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635692.Peer-Reviewed Original ResearchT)-space dataExtracellular volume mappingIntrinsic low-dimensional manifold structureCardiac tissue propertiesImproved image reconstructionLow-dimensional manifold structureExtracellular volumeFast MRI methodSparsity constraintModel-based methodsSuperior performanceSpace alignmentT1 mappingManifold structureImage reconstructionT)-spacePost-contrast T1 mappingTissue propertiesFree breathingConcentration of contrast agentLongitudinal relaxation timeAlignment modelDynamic MR imagingSparsityAlignment matrix
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
2020
Accelerating MRI Reconstruction on TPUs
Lu T, Marin T, Zhuo Y, Chen Y, Ma C. Accelerating MRI Reconstruction on TPUs. 2020, 00: 1-9. DOI: 10.1109/hpec43674.2020.9286192.Peer-Reviewed Original ResearchTensor Processing UnitK-space dataData decompositionMeasured k-space dataImage reconstructionAccelerated MRI reconstructionGoogle’s Tensor Processing UnitMR image reconstructionScientific computing problemsAlternating direction methodMachine learning applicationsReconstruction methodImage reconstruction methodDiscrete Fourier transformSparsifying transformCompressive sensingFourier transform operationSparsity constraintMRI reconstructionLearning applicationsCommunication timeNetwork topologyProcessing unitMatrix multiplicationComputational problems
2019
Free-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
2016
Accelerated 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
Spectral Estimation for Magnetic Resonance Spectroscopic Imaging with Spatial Sparsity Constraints
Ning Q, Ma C, Liang Z. Spectral Estimation for Magnetic Resonance Spectroscopic Imaging with Spatial Sparsity Constraints. 2015, 1482-1485. DOI: 10.1109/isbi.2015.7164157.Peer-Reviewed Original ResearchSignal-to-noise ratioState-of-the-art methodsState-of-the-artLow signal-to-noise ratioSpatial sparsity constraintsJoint estimation problemSpectral estimationRegularization frameworkSparsity constraintEstimation accuracyEstimation problemRobust solutionExperimental resultsSpatial constraintsModel nonlinearityConstraintsSpectral characteristicsQuantitative problemsImagesParametersNonlinearitySimulation
2013
PERFORMANCE ANALYSIS OF DENOISING WITH LOW-RANK AND SPARSITY CONSTRAINTS
Lam F, Ma C, Liang Z. PERFORMANCE ANALYSIS OF DENOISING WITH LOW-RANK AND SPARSITY CONSTRAINTS. 2013, 1223-1226. DOI: 10.1109/isbi.2013.6556701.Peer-Reviewed Original ResearchLow-rankDenoising methodSparsity constraintLow-rank propertyNoise reductionConstrained Cramer-RaoImpressive empirical resultsDenoising effectDenoising capabilityDenoisingSparsityTheoretical boundsMaximum noise reductionCramer-RaoNumerical simulationsUpper boundImaging applicationsConstraintsEmpirical resultsBoundsNoiseMethodCapabilityImages