2024
Anatomically and Metabolically Informed Deep Learning Low-Count PET Image Denoising
Xia M, Xie H, Liu Q, Guo L, Ouyang J, Bayerlein R, Spencer B, Badawi R, Li Q, Fakhri G, Liu C. Anatomically and Metabolically Informed Deep Learning Low-Count PET Image Denoising. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10657099.Peer-Reviewed Original ResearchDeep learningOver-smoothed imagesDL training processesHigh-count imagesImage denoisingDenoised imageLow-count dataSemantic informationSemantic classesSegmentation guidanceTraining processPET/CT systemHistogram distributionImage qualitySegmentation toolPositron emission tomographyImagesDenoisingDatasetHistogramPriorsRadiation exposure
2015
National Electrical Manufacturers Association and Clinical Evaluation of a Novel Brain PET/CT Scanner
Grogg K, Toole T, Ouyang J, Zhu X, Normandin M, Li Q, Johnson K, Alpert N, Fakhri G. National Electrical Manufacturers Association and Clinical Evaluation of a Novel Brain PET/CT Scanner. Journal Of Nuclear Medicine 2015, 57: 646-652. PMID: 26697961, PMCID: PMC4818715, DOI: 10.2967/jnumed.115.159723.Peer-Reviewed Original ResearchConceptsNoise-equivalent count rateCount rateLoose cutsMaximum noise-equivalent counting rateSpatial resolutionDetector ringSilicon photomultipliersBrain phantomContrast recoveryAttenuation correctionPET/CT systemCrystal blockPET/CT scannerImage qualityRadial offsetNational Electrical Manufacturers AssociationActivity distributionUnique mobility capabilitiesAxial extentTransverse resolutionPhantomAxial resolutionActivity concentrationsHuman scansLayer 1 cm