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
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