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
2014
Accuracy of Respiratory Motion Compensated Image Reconstruction Using 4DPET-Derived Deformation Fields
Dutta J, Chelala M, Shao X, Lorsakul A, Li Q, Fakhri G. Accuracy of Respiratory Motion Compensated Image Reconstruction Using 4DPET-Derived Deformation Fields. 2014, 1-4. DOI: 10.1109/nssmic.2014.7430924.Peer-Reviewed Original ResearchAttenuation-corrected PET imagesXCAT phantomNon-attenuation-corrected PET imagesMotion-compensated image reconstructionImage reconstructionMotion informationPET imagingMotion estimationPET gatingAttenuation mapBreathing motionPET quantitationSoftware GATEXCATDeformation fieldPhantomAccuracy of motionGround truthGateAnatomical imagesFieldMotionUpper abdomenPulmonary lesionsMonte