2022
Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography
Liu X, Marin T, Amal T, Woo J, Fakhri G, Ouyang J. Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography. Medical Physics 2022, 50: 1539-1548. PMID: 36331429, PMCID: PMC10087283, DOI: 10.1002/mp.16078.Peer-Reviewed Original ResearchConceptsConditional variational auto-encoderDeep learning approachNeural networkDeep learningMarkov chain Monte CarloVariational Bayesian inference frameworkLearning approachDeep learning-based approachVariational auto-encoderDeep neural networksLearning-based approachDynamic brain PET imagingPosterior distributionEstimate posterior distributionsBayesian inference frameworkAuto-encoderMedical imagesInference frameworkNetworkSimulation studyBrain PET imagingLearningPosterior estimatesInferior performanceImagesMeasuring strain in diffusion-weighted data using tagged magnetic resonance imaging
Xing F, Liu X, Reese T, Stone M, Wedeen V, Prince J, El Fakhri G, Woo J. Measuring strain in diffusion-weighted data using tagged magnetic resonance imaging. Proceedings Of SPIE--the International Society For Optical Engineering 2022, 12032: 1203205-1203205-7. PMID: 36777787, PMCID: PMC9911263, DOI: 10.1117/12.2610989.Peer-Reviewed Original ResearchMR spaceMotion fieldDeep neural networksEstimated motion fieldNeural networkDiffusion-weighted dataMedical imagesDynamic MR dataAlgorithm workflowStatic imagesDeformable organsTag dataData lack informationDiffeomorphic registrationEstimated strain valuesDiffusion tractographyInternal tongue musclesMR dataFiber tractographyMotion analysisTongue deformationAccurate strain measurementsMuscle fibersImaging excelsMagnetic resonance
2018
End-to-End Lung Nodule Detection in Computed Tomography
Wu D, Kim K, Dong B, Fakhri G, Li Q. End-to-End Lung Nodule Detection in Computed Tomography. Lecture Notes In Computer Science 2018, 11046: 37-45. DOI: 10.1007/978-3-030-00919-9_5.Peer-Reviewed Original ResearchDeep reconstruction networkLung nodule detectionReconstruction networkEnd-to-end detectorMedical imagesLung Image Database Consortium image collectionNodule detectionEfficient network trainingReconstructed imagesConvolutional neural networkEnd-to-endSuperior detection performanceRaw dataComputer visionCAD systemCNN detectorNetwork trainingImage collectionNeural networkDetection performanceImage spaceDetection taskDetection systemModern medical imagingFanbeam projectionsIterative PET Image Reconstruction Using Convolutional Neural Network Representation
Gong K, Guan J, Kim K, Zhang X, Yang J, Seo Y, Fakhri G, Qi J, Li Q. Iterative PET Image Reconstruction Using Convolutional Neural Network Representation. IEEE Transactions On Medical Imaging 2018, 38: 675-685. PMID: 30222554, PMCID: PMC6472985, DOI: 10.1109/tmi.2018.2869871.Peer-Reviewed Original ResearchConceptsPET image reconstructionNeural networkConvolutional neural network representationsDeep residual convolutional neural networkImage reconstructionResidual convolutional neural networkComputer vision tasksDeep neural networksConvolutional neural networkNeural network denoisersAlternating direction methodNeural network representationIterative reconstruction frameworkNeural network methodVision tasksImage representationNetwork denoisingReconstruction frameworkMultipliers algorithmMedical imagesOptimization problemNetwork methodPost-processing toolDirection methodNetwork representationPenalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting
Kim K, Wu D, Gong K, Dutta J, Kim J, Son Y, Kim H, Fakhri G, Li Q. Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting. IEEE Transactions On Medical Imaging 2018, 37: 1478-1487. PMID: 29870375, PMCID: PMC6375088, DOI: 10.1109/tmi.2018.2832613.Peer-Reviewed Original ResearchConceptsDeep learningDenoising convolutional neural networkConvolutional neural networkDeep learning-basedPerformance of iterative reconstructionPotential of deep learningDeep networksNoise levelLearning-basedReconstruction frameworkDegradation of performanceNeural networkDnCNNMedical imagesDownsampled dataFitness functionPoisson thinningFull-dose imagesLow dose imagesNoise conditionsNetworkImage qualityPET reconstructionDose imagesDeep
2016
Validation of Bayesian analysis of compartmental kinetic models in medical imaging
Sitek A, Li Q, Fakhri G, Alpert N. Validation of Bayesian analysis of compartmental kinetic models in medical imaging. Physica Medica 2016, 32: 1252-1258. PMID: 27692754, PMCID: PMC5720163, DOI: 10.1016/j.ejmp.2016.09.010.Peer-Reviewed Original ResearchConceptsAccurate estimation of uncertaintyComputer simulationsMedical imagesPosterior distributionDistributed noiseTime series of imagesClosed-formSeries of imagesData setsKinetic parametersMarkov chain Monte Carlo methodsPosterior distributions of kinetic parametersNon-linear least squares methodAccurate estimationComputerLeast-squares methodKinetic modelEstimation of kinetic parametersF18-fluorodeoxyglucoseBayesian estimationImagesStatistical inferenceMonte Carlo methodEstimates of uncertaintyInformation