2024
A deep learning-based approach to nuisance signal removal from MRSI data aqcuired without suppression
Lee W, Zhuo Y, Marin T, Han P, Chi D, Fakhri G, Ma C. A deep learning-based approach to nuisance signal removal from MRSI data aqcuired without suppression. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/0259.Peer-Reviewed Original ResearchDeep learning-based methodsLearning-based methodsU-Net structureSignal removalIn vivo MRSI dataNeural networkU-NetMRSI dataImage reconstructionSuperior performanceData processingRobust performanceHankel matrixNetworkNuisance signalsConventional methodsPerformanceMRSI signalsSignalMethodRemove nuisance signalsRemovalHankel
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
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
2015
Improved 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 imagingEncoding 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
2014
Improved 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 imagingOptimizationRegularizationMethodCapabilityField-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 Research
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