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
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 regions
2018
Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images
Gong K, Yang J, Kim K, Fakhri G, Seo Y, Li Q. Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images. Physics In Medicine And Biology 2018, 63: 125011. PMID: 29790857, PMCID: PMC6031313, DOI: 10.1088/1361-6560/aac763.Peer-Reviewed Original ResearchConceptsU-Net structureU-NetModified U-net structureAttenuation correctionDeep neural network methodBrain PET imagingPET attenuationDeep neural networksPatient data setsAttenuation coefficientDixon-based methodNeural network methodData setsConvolution moduleNetwork inputNeural networkDixon MRPET/MR hybrid systemImage reconstructionPET imagingNetwork methodNetworkNetwork approachNetwork structureQuantification errors
2017
Multi-Materials Decomposition using clinical Dualenergy CT
Zhao T, Kim K, Wu D, Kalra M, Fakhri G, Li Q. Multi-Materials Decomposition using clinical Dualenergy CT. 2017, 00: 1-4. DOI: 10.1109/nssmic.2017.8532936.Peer-Reviewed Original ResearchAttenuation coefficient functionDecomposition methodMulti-materials decomposition methodDomain decompositionMulti-materialAttenuation coefficientMaterial informationScanned objectX-ray sourcesMaterialsX-rayMedical applicationsEffective attenuation coefficientCoefficient functionsDecompositionMulti-material decompositionCoefficientDensity-based clustering
2014
Spectral CT Using Multiple Balanced K-Edge Filters
Rakvongthai Y, Worstell W, Fakhri G, Bian J, Lorsakul A, Ouyang J. Spectral CT Using Multiple Balanced K-Edge Filters. IEEE Transactions On Medical Imaging 2014, 34: 740-747. PMID: 25252276, PMCID: PMC4349342, DOI: 10.1109/tmi.2014.2358561.Peer-Reviewed Original ResearchConceptsK-edge filtersReconstructed attenuation coefficientsEnergy binsAttenuation coefficientMultiple energy binsX-ray sourcesX-ray tubeBack-projection reconstructionSpectral CT imagingTransmission matrixModel expectationsSinogram binsBeam hardeningComplex phantomsSpectral computed tomographyK-edgeAttenuation imagesSpectral CTCT scannerX-rayConventional detectorsBack-projectionSinogramPhantomCost-effective system design