Chao Ma, PhD
Assistant Professor of Radiology and Biomedical ImagingCards
About
Research
Publications
2024
Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657955.Peer-Reviewed Original ResearchConditional variational autoencoderEfficient deep learning-based approachMarkov chain Monte CarloDenoising diffusion probabilistic modelDeep learning-based approachDiffusion probabilistic modelLearning-based approachApproximate posterior distributionPosterior distributionVariational autoencoderHeavy computationTau protein aggregationBayesian inferenceProbabilistic modelData-drivenStudy molecular processesBayesian posterior distributionProtein aggregationMetropolis-Hastings Markov chain Monte CarloMolecular processesAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersEstimate posterior distributionsAutoencoderMultimodality Molecular Imaging of Brain Tumor Using Simultaneous [18F]FET-PET/MRSI
Ma C, Han P, Marin T, Zhuo Y, Shih H, Fakhri G. Multimodality Molecular Imaging of Brain Tumor Using Simultaneous [18F]FET-PET/MRSI. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10656528.Peer-Reviewed Original ResearchList-mode dataMR spectroscopic imagingSpatial resolutionAccurate brain tumor delineationMR physicsIsotropic resolutionBrain tumor delineationImprove treatment planningSpectroscopic imagingTumor delineationSignal-to-noise ratioIntact blood-brain barrierImaging speedAmino acid radiotracerImaging timeMR signalHigher proliferation activityStructural MRTreatment planningBlood-brain barrierMR spectroscopic imaging dataMolecular imaging of brain tumorsTumor involvementTumor infiltrationTumor marginsPET 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 reconstructionFree‐breathing 3D cardiac extracellular volume (ECV) mapping using a linear tangent space alignment (LTSA) model
Lee W, Han P, Marin T, Mounime I, Eslahi S, Djebra Y, Chi D, Bijari F, Normandin M, Fakhri G, Ma C. Free‐breathing 3D cardiac extracellular volume (ECV) mapping using a linear tangent space alignment (LTSA) model. Magnetic Resonance In Medicine 2024, 93: 536-549. PMID: 39402014, PMCID: PMC11606777, DOI: 10.1002/mrm.30284.Peer-Reviewed Original ResearchExtracellular volume mappingContrast agent injectionExtracellular volumeGradient echo readoutECV mapsAgent injectionWhole heartEcho readoutExtracellular volume valuesVoxel-by-voxelInversion recovery sequenceSpatial resolutionScan timeImaging timeIn vivo studiesHealthy volunteersModel-based methodsRecovery sequenceInjectionReadoutPoint-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model
Liu X, Woo J, Ma C, Ouyang J, Fakhri G. Point-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model. 2011 IEEE Nuclear Science Symposium Conference Record 2024, 00: 1-1. PMID: 39445308, PMCID: PMC11497479, DOI: 10.1109/nss/mic/rtsd57108.2024.10656071.Peer-Reviewed Original ResearchAccelerated 3D metabolite T1 mapping of the brain using variable‐flip‐angle SPICE
Zhao Y, Li Y, Guo R, Jin W, Sutton B, Ma C, Fakhri G, Li Y, Luo J, Liang Z. Accelerated 3D metabolite T1 mapping of the brain using variable‐flip‐angle SPICE. Magnetic Resonance In Medicine 2024, 92: 1310-1322. PMID: 38923032, DOI: 10.1002/mrm.30200.Peer-Reviewed Original ResearchConceptsLow-rank tensor modelGeneralized series modelMetabolite TExperimental resultsBrain metabolitesClinically acceptable scan timeEfficient encodingPhantom experimental resultsAcceptable scan timeNoisy dataSparse samplingImaging problemsData processingHealthy subject dataVariable flip angleFlip angleTensor modelSaturation effectsQuantitative metabolic imagingMRSI techniquePhantomScan timeData acquisitionMetabolic imagingT1 mappingSparsity 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 matrixFree-breathing 3D cardiac extracellular volume (ECV) mapping using a linear tangent space alignment (LTSA) model
Lee W, Han P, Marin T, Mounime I, Eslahi S, Djebra Y, Chi D, Fakhri G, Ma C. Free-breathing 3D cardiac extracellular volume (ECV) mapping using a linear tangent space alignment (LTSA) model. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/1493.Peer-Reviewed Original ResearchA 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 signalsRemovalHankelDiffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2024, 00: 1-5. PMID: 39530051, PMCID: PMC11554386, DOI: 10.1109/isbi56570.2024.10635805.Peer-Reviewed Original ResearchPosterior distributions of kinetic parametersDenoising diffusion probabilistic modelHyperphosphorylated tauP-tauDiffusion probabilistic modelAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersPosterior distributionInference efficiencyComputational needsEstimate kinetic parametersProbabilistic modelComputation time