2023
Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model
Dong Y, Jang S, Han P, Johnson K, Ma C, Fakhri G, Li Q, Gong K. Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338188.Peer-Reviewed Original ResearchDiffusion probabilistic modelGenerative adversarial networkConditional encodingAttenuation correctionDenoising diffusion probabilistic modelLow-level featuresProbabilistic modelAttenuation coefficientAdversarial networkExtract featuresPET/MR systemsEncodingPET acquisitionNovel methodDiffusion encodingMagnetic resonanceImagesPET imagingCorrectionMR imagingUNetAttenuationNetworkFeaturesResonance
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
2019
Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning
Gong K, Han P, Fakhri G, Ma C, Li Q. Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning. NMR In Biomedicine 2019, 35: e4224. PMID: 31865615, PMCID: PMC7306418, DOI: 10.1002/nbm.4224.Peer-Reviewed Original ResearchConceptsSignal-to-noise ratioImage denoisingReconstruction frameworkDeep learning-based image denoisingDeep learning-based denoisersMR image denoisingLearning-based denoisingLow signal-to-noise ratioK-space dataNoisy imagesTraining labelsTraining pairsNetwork inputNeural networkDenoisingIn vivo experiment dataSuperior performanceImaging speedReconstruction processImage qualityLong imaging timesNetworkFrameworkImagesSpatial resolution
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
2016
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 2016, 76: spcone-spcone. DOI: 10.1002/mrm.26460.Peer-Reviewed Original ResearchImage reconstructionSubspace structureSpectroscopic imaging sequenceSubspace modelImage sequencesEdge-preserving regularizationReconstruction methodThrough-plane resolutionData acquisitionImage reconstruction methodIn-planeIn vivo brain experimentsEncoding schemeField inhomogeneity correctionIn-plane resolutionTwo-dimensional (2DImaging frameworkInhomogeneity correctionData setsSubspaceHybrid data setsSpectroscopic imagingSpatial resolutionBrain experimentsImagesAccelerated 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
Encoding 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 imagingCodeDataMethodReconstructionSpicesSpectral 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
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 imagingOptimizationRegularizationMethodCapability
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