2021
Magnetic resonance parameter mapping using model‐guided self‐supervised deep learning
Liu F, Kijowski R, Fakhri G, Feng L. Magnetic resonance parameter mapping using model‐guided self‐supervised deep learning. Magnetic Resonance In Medicine 2021, 85: 3211-3226. PMID: 33464652, PMCID: PMC9185837, DOI: 10.1002/mrm.28659.Peer-Reviewed Original ResearchConceptsMR parameter mappingSupervised learningReconstruction qualityImaging modelSelf-supervised deep learningStandard supervised learningConventional iterative reconstructionData setsDeep learning purposesSuperior reconstruction qualityImprove reconstruction qualityQuantitative MRI applicationsUndersampled k-spacePresence of noisePhysical modeling constraintsSparsity constraintNetwork trainingReconstruction performanceDeep learningReconstruction frameworkMap extractionImprove image qualitySuppress noiseGround truthUndersampling artifacts
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