2024
Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI
Liu X, Xing F, Zhuo J, Stone M, Prince J, El Fakhri G, Woo J. Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI. Proceedings Of SPIE--the International Society For Optical Engineering 2024, 12926: 129262w-129262w-5. PMID: 39238547, PMCID: PMC11377028, DOI: 10.1117/12.3006874.Peer-Reviewed Original ResearchCross-modal translationHealthy individualsTongue cancer patientsMotion fieldOut-of-distributionOne-class SVMPatient dataAnomaly detectionAnomaly detectorCancer patientsTagged MRISpeech-related disordersGeneralization capabilityReconstruction qualitySpeech qualityArticulatory-acoustic relationsPatientsSpeech waveformTraining setInnovative treatmentsMRITest setMotion patternsArticulatory featuresTraining translators
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
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