2019
Computational-efficient cascaded neural network for CT image reconstruction
Wu D, Kim K, Fakhri G, Li Q. Computational-efficient cascaded neural network for CT image reconstruction. Progress In Biomedical Optics And Imaging 2019, 10948: 109485z-109485z-6. DOI: 10.1117/12.2511526.Peer-Reviewed Original ResearchCascaded neural networkNeural networkImage reconstructionCT image reconstructionMemory consumptionDevelopment of deep learningDeep artificial neural networksState-of-the-artMedical image reconstructionReduce memory consumptionImage reconstruction qualitySparse-view samplingTraining ground truthUnrolling networkImage priorsImage quality improvementImage patchesReconstruction qualityDeep learningArtificial neural networkImage domainUndersampled projectionsTraining phaseTraining processParameter tuning
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
2001
Absolute Quantitation in Simultaneous 99mTc/123I Brain SPECT Using ANN: Design Optimization and Validation
Fakhri G, Maksud P, Moore S, Zimmerman R, Kijewski M. Absolute Quantitation in Simultaneous 99mTc/123I Brain SPECT Using ANN: Design Optimization and Validation. 2001, 3: 1429-1431. DOI: 10.1109/nssmic.2001.1008605.Peer-Reviewed Original ResearchMonte Carlo simulationsBrain phantomSimultaneous dual-isotope imagingDigital brain phantomDual-isotope SPECT studyVariable collimator responseCarlo simulationsNon-uniform attenuationI-123 imagingEnergy windowCollimator responseDual-isotope imagingPhantom acquisitionsOSEM algorithmArtificial neural networkPhysical acquisitionScatter correctionClinical cameraBrain structuresPhantomIsotope imagingMonteSPECT studiesActivity concentrationsTc-99Comparative Assessment of Energy-Based Methods of Compensating for Scatter and Lead X-Rays in Ga-67 SPECT Imaging
Moore S, Fakhri G, Maksud P. Comparative Assessment of Energy-Based Methods of Compensating for Scatter and Lead X-Rays in Ga-67 SPECT Imaging. 2001, 4: 2197-2198. DOI: 10.1109/nssmic.2001.1009260.Peer-Reviewed Original ResearchLead X-raysGa-67Energy windowArtificial neural networkGa-67 SPECT imagingSPECT imagesHigh-energy contaminationGa-67 SPECTPoisson noise realizationsActivity estimation taskTumor activity concentrationAnthropomorphic phantomEvaluable tumorsGS methodTumorMean square errorData setsOrgan uptakeProjection imagesLymphoma studiesNeural networkPixel valuesX-raySpherical tumorNoise realizations
2000
A new scatter compensation method for Ga-67 imaging using artificial neural networks
Fakhri G, Moore S, Maksud P. A new scatter compensation method for Ga-67 imaging using artificial neural networks. 2011 IEEE Nuclear Science Symposium Conference Record 2000, 2: 13/48-13/52 vol.2. DOI: 10.1109/nssmic.2000.949989.Peer-Reviewed Original ResearchK-shell X-raysEnergy window imagesAnthropomorphic torso phantomScatter correction methodPoisson noise realizationsGa-67 studiesPhotoelectric absorptionPhoton interactionsWindow imagesTorso phantomArtificial neural networkCoherent scatteringMonte Carlo simulationsScatter correctionGa-67Reconstructed volumeNeural networkCarlo simulationsCollimatorNoise realizationsScatteringGa-67 imagingArtificial neural network learningPrimary distributionError backpropagation