2024
PET motion correction using subspace-based real-time MR imaging in simultaneous PET/MR
Mounime I, Marin T, Han P, Ouyang J, Gori P, Angelini E, Fakhri G, Ma C. PET motion correction using subspace-based real-time MR imaging in simultaneous PET/MR. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657647.Peer-Reviewed Original ResearchOrdered-subset expectation maximizationMotion correctionGated reconstructionsMotion-corrected PET reconstructionsPET eventsCardiac motion phasesMotion correction methodCardiac motionMotion phaseReconstructed dynamic imagesPET reconstructionReal-time MR imagingSimultaneous PET/MRPatient motionSoft tissue contrastDynamic MR image reconstructionReference phaseMitigate artifactsLow-rank propertyMR image reconstructionPositron emission tomographyManifold learning frameworkSpatial resolutionBlurring artifactsImage reconstructionSparsity 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
2023
Direct estimation of metabolite maps from undersampled k-space data using linear tangent space alignment
Ma C, Marin T, Han P, Fakhri G. Direct estimation of metabolite maps from undersampled k-space data using linear tangent space alignment. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/0496.Peer-Reviewed Original ResearchArterial spin labeled perfusion imaging with balanced steady-state free precession readout and radial sampling
Han P, Marin T, Zhuo Y, Ouyang J, El Fakhri G, Ma C. Arterial spin labeled perfusion imaging with balanced steady-state free precession readout and radial sampling. Magnetic Resonance Imaging 2023, 102: 126-132. PMID: 37187264, PMCID: PMC10524790, DOI: 10.1016/j.mri.2023.05.005.Peer-Reviewed Original ResearchConceptsOff-resonance effectsBalanced steady-state free precessionPhase-cycling techniqueTemporal SNRBalanced steady-state free precession acquisitionRadial sampling schemeSpoiled gradient-recalled acquisitionRadial samplingCartesian sampling schemeBalanced steady-state free precession readoutK-space dataSampling schemeSpin labelingSteady-state free precessionK-spaceImage readoutBanding artifactsMotion-related artifactsReadoutFree precessionArterial spin labelingImage reconstructionParallel imagingImaging timePerfusion-weighted imaging
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
2021
Nonuniform Fast Fourier Transform on Tpus
Lu T, Marin T, Zhuo Y, Chen Y, Ma C. Nonuniform Fast Fourier Transform on Tpus. 2021, 00: 783-787. DOI: 10.1109/isbi48211.2021.9434068.Peer-Reviewed Original ResearchNonuniform fast Fourier transformFast Fourier transformTensor Processing UnitTPU coresFourier transformImage reconstructionMR image reconstructionTensor operatorsK-spaceK-space dataGoogle’s Tensor Processing UnitDeep learning applicationsNumerical examplesNonuniform gridsScaling analysisCPU implementationMagnetic resonanceHardware acceleratorsLearning applicationsComputational bottleneckProcessing unitMatrix multiplicationPractical runtimeAccelerationOperation
2020
MR‐based PET attenuation correction using a combined ultrashort echo time/multi‐echo Dixon acquisition
Han P, Horng D, Gong K, Petibon Y, Kim K, Li Q, Johnson K, Fakhri G, Ouyang J, Ma C. MR‐based PET attenuation correction using a combined ultrashort echo time/multi‐echo Dixon acquisition. Medical Physics 2020, 47: 3064-3077. PMID: 32279317, PMCID: PMC7375929, DOI: 10.1002/mp.14180.Peer-Reviewed Original ResearchConceptsLinear attenuation coefficientPositron emission tomography attenuation correctionPhysical compartmental modelAttenuation correctionShort T<sub>2</sub> componentPET attenuation correctionRadial k-space trajectoryMagnetic resonance (MR)-based methodK-space trajectoriesRadial trajectoryK-spaceAttenuation coefficientDixon acquisitionsPositron emission tomographyWhole white matterMuting methodImage reconstructionImaging speedMR signalMRAC methodPositron emission tomography imagingCorrectionGray matter regionsPhantomMatter regionsAccelerating 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
Body motion detection and correction in cardiac PET: Phantom and human studies
Sun T, Petibon Y, Han P, Ma C, Kim S, Alpert N, Fakhri G, Ouyang J. Body motion detection and correction in cardiac PET: Phantom and human studies. Medical Physics 2019, 46: 4898-4906. PMID: 31508827, PMCID: PMC6842053, DOI: 10.1002/mp.13815.Peer-Reviewed Original ResearchConceptsList-mode dataMotion-compensated image reconstructionMotion correctionCenter of massPET list-mode dataMotion correction methodMotion detectionMotion estimationImage reconstructionPatient body motionDegrade image qualityNonrigid registrationImage qualityMotion transformationCoincident distributionBody motion detectionCardiac positron emission tomographyBack-projection techniqueCovariance matrixImage volumesBody motionPositron emission tomographyBack-projectionReference framePhantom
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
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 2015, 76: 1059-1070. PMID: 26509928, PMCID: PMC4848237, DOI: 10.1002/mrm.26019.Peer-Reviewed Original ResearchConceptsSubspace structureSpectroscopic imaging sequenceImage reconstructionSubspace modelImage sequencesImage reconstruction purposesEdge-preserving regularizationData acquisitionReconstruction methodThrough-plane resolutionImage reconstruction methodIn-planeIn vivo brain experimentsEncoding schemeField inhomogeneity correctionIn-plane resolutionTwo-dimensional (2DImaging frameworkInhomogeneity correctionData setsSubspaceHigh-resolutionHybrid data setsSpatial resolutionBrain experimentsEncoding 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 imagingOptimizationRegularizationMethodCapability