2020
Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging
Gong K, Han P, Johnson K, El Fakhri G, Ma C, Li Q. Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging. European Journal Of Nuclear Medicine And Molecular Imaging 2020, 48: 1351-1361. PMID: 33108475, PMCID: PMC8411350, DOI: 10.1007/s00259-020-05061-w.Peer-Reviewed Original ResearchConceptsAttenuation correctionResultsThe Dice coefficientPseudo-CT imagesMR-based AC methodsAccurate ACAC accuracyPET imagingDice coefficientQuantitative accuracyAtlas methodAC methodGradient echoNear verticesTau imagingTau PET imagingAlzheimer's diseaseUltrashortCorrectionTau pathologyRapid acquisitionDeep learning methodsMonitoring of Alzheimer’s diseasePET/MRAmyloid
2019
Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction
Blanc-Durand P, Khalife M, Sgard B, Kaushik S, Soret M, Tiss A, Fakhri G, Habert M, Wiesinger F, Kas A. Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction. PLOS ONE 2019, 14: e0223141. PMID: 31589623, PMCID: PMC6779234, DOI: 10.1371/journal.pone.0223141.Peer-Reviewed Original ResearchConceptsZero echo timeAC mapsAttenuation correctionPET attenuation correctionCT-based ACComputed tomographyAC methodPhoton attenuationZTE-ACInvestigation of suspected dementiaMR imagingBrain computed tomographyAtlas-ACBrain metabolismZTE-MRIConvolutional neural networkEcho timeHead atlasFDG-PET/MRPET imagingLow biasRegions-of-interestPatientsCorrectionNeural network