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
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 studyImagesMethodSparsity
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
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