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
2007
Fast Analytical Modeling of Compton Scatter Using Point Clouds and Graphics Processing Unit (GPU)
Sitek A, Fakhri G, Ouyang J, Maltz J. Fast Analytical Modeling of Compton Scatter Using Point Clouds and Graphics Processing Unit (GPU). 2007, 6: 4546-4548. DOI: 10.1109/nssmic.2007.4437122.Peer-Reviewed Original ResearchGraphics processing unitsComputation timeProcessing unitPoint cloudsNVIDIA Quadro FXCalculation speedEfficient geometric modelingScattered point cloudExorbitant computation timeComputing architectureImage samplesImaging modelResolution levelsComputational frameworkComputerGraphicsComputer simulationsArchitectureRepresentationCloudImage volumesNatural advantagesComputational modelCompton scatteringAccurate model