2017
Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network
Wu D, Kim K, Fakhri G, Li Q. Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network. IEEE Transactions On Medical Imaging 2017, 36: 2479-2486. PMID: 28922116, PMCID: PMC5897914, DOI: 10.1109/tmi.2017.2753138.Peer-Reviewed Original ResearchConceptsArtificial neural networkIterative reconstruction algorithmNeural networkLow-dose CT reconstructionReconstruction algorithmUnsupervised feature learningReconstructed imagesFeatures of imagesImprove reconstruction qualityNormal-dose imagesDecreasing radiation riskDevelopment of artificial neural networksFeature learningComplex featuresAuto-encoderReconstruction qualityData fidelityMachine learningSuppress noiseSmoothness constraintPhoton fluxPreservation abilityGrand ChallengeNoise reductionPriors
2002
Optimization of Ga‐67 imaging for detection and estimation tasks: Dependence of imaging performance on spectral acquisition parameters
Fakhri G, Moore S, Kijewski M. Optimization of Ga‐67 imaging for detection and estimation tasks: Dependence of imaging performance on spectral acquisition parameters. Medical Physics 2002, 29: 1859-1866. PMID: 12201433, DOI: 10.1118/1.1493214.Peer-Reviewed Original ResearchConceptsIdeal signal-to-noise ratioEnergy windowSignal-to-noise ratioMonte Carlo programDetection of spheresTorso phantomPhantom acquisitionsSphere of radiusEstimation taskPhantom dataLower-energyGa-67 imagingPhantomAcquisition parametersActivity concentrationsSpectral acquisition parametersGa-67Sphere sizeEnergyPhotopeakTumor imagingOptimal windowTaskClinicMonte