2024
Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification
Liu Q, Tsai Y, Gallezot J, Guo X, Chen M, Pucar D, Young C, Panin V, Casey M, Miao T, Xie H, Chen X, Zhou B, Carson R, Liu C. Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification. Medical Image Analysis 2024, 95: 103180. PMID: 38657423, DOI: 10.1016/j.media.2024.103180.Peer-Reviewed Original ResearchDeep Image PriorImage priorsSupervised modelsNoise reductionIntrinsic image featuresDeep learning techniquesU-Net architectureNovel denoising techniqueQuality of parametric imagesDenoising modelDenoising techniquesStatic datasetsBaseline techniquesEffective noise reductionData-driven approachLearning techniquesDynamic datasetsOptimization processPrior informationStatic imagesHigh noise levelsImage featuresDatasetPrior imagePET datasets
2023
MCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion Correction
Guo X, Zhou B, Chen X, Chen M, Liu C, Dvornek N. MCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion Correction. IEEE Transactions On Medical Imaging 2023, 42: 3512-3523. PMID: 37368811, PMCID: PMC10751388, DOI: 10.1109/tmi.2023.3290003.Peer-Reviewed Original ResearchMotion estimation blockDeep learning benchmarksGood generalization capabilityMotion correctionMotion correction frameworkMotion prediction errorGeneralization capabilityNetwork performanceNeural networkMotion correction techniqueLearning benchmarksRegistration problemLoss functionEstimation blockLoss optimizationPenalty componentDynamic frameFitting errorSpatial alignmentParametric imagesSpatial misalignmentDynamic positron emission tomographySubject motionPrediction errorCorrection frameworkGeneration of Whole-Body FDG Parametric Ki Images From Static PET Images Using Deep Learning
Miao T, Zhou B, Liu J, Guo X, Liu Q, Xie H, Chen X, Chen M, Wu J, Carson R, Liu C. Generation of Whole-Body FDG Parametric Ki Images From Static PET Images Using Deep Learning. IEEE Transactions On Radiation And Plasma Medical Sciences 2023, 7: 465-472. PMID: 37997577, PMCID: PMC10665031, DOI: 10.1109/trpms.2023.3243576.Peer-Reviewed Original Research
2022
Inter-Pass Motion Correction for Whole-Body Dynamic PET and Parametric Imaging
Guo X, Wu J, Chen M, Liu Q, Onofrey J, Pucar D, Pang Y, Pigg D, Casey M, Dvornek N, Liu C. Inter-Pass Motion Correction for Whole-Body Dynamic PET and Parametric Imaging. IEEE Transactions On Radiation And Plasma Medical Sciences 2022, 7: 344-353. PMID: 37842204, PMCID: PMC10569406, DOI: 10.1109/trpms.2022.3227576.Peer-Reviewed Original ResearchVirtual high‐count PET image generation using a deep learning method
Liu J, Ren S, Wang R, Mirian N, Tsai Y, Kulon M, Pucar D, Chen M, Liu C. Virtual high‐count PET image generation using a deep learning method. Medical Physics 2022, 49: 5830-5840. PMID: 35880541, PMCID: PMC9474624, DOI: 10.1002/mp.15867.Peer-Reviewed Original ResearchConceptsStructural similarity indexImage quality evaluationDeep learning-based methodsDeep learning methodsImage qualityLearning-based methodsPET datasetsStatic datasetsDL methodsNet networkImage generationPET imagesNetwork inputsImage counterpartsLearning methodsNetwork outputTraining datasetPeak signalPositron emission tomography (PET) imagesQuality evaluationDatasetCross-validation resultsMean square errorHigh-count imagesImages
2021
PET Image Denoising Using a Deep-Learning Method for Extremely Obese Patients
Liu H, Yousefi H, Mirian N, Lin M, Menard D, Gregory M, Aboian M, Boustani A, Chen M, Saperstein L, Pucar D, Kulon M, Liu C. PET Image Denoising Using a Deep-Learning Method for Extremely Obese Patients. IEEE Transactions On Radiation And Plasma Medical Sciences 2021, 6: 766-770. PMID: 37284026, PMCID: PMC10241407, DOI: 10.1109/trpms.2021.3131999.Peer-Reviewed Original ResearchSuper-resolution PET Brain Imaging using Deep Learning
Ren S, Liu J, Xie H, Toyonaga T, Mirian N, Chen M, Aboian M, Carson R, Liu C. Super-resolution PET Brain Imaging using Deep Learning. 2021, 00: 1-6. DOI: 10.1109/nss/mic44867.2021.9875548.Peer-Reviewed Original ResearchDeep learning networkPET image resolutionData augmentation methodImage resolutionSuper-resolution approachMedical imaging modalitiesClinical brain imagesDeep learningLearning networkAugmentation methodPET image qualityBrain imagesImage qualityNetworkImagesMedical diagnostic technologyPET imagesHRRT imagesData generalizabilityLearningSubstantial improvementScannerTechnologyPET brain imagingAccuracySynthesizing Multi-tracer PET Images for Alzheimer’s Disease Patients Using a 3D Unified Anatomy-Aware Cyclic Adversarial Network
Zhou B, Wang R, Chen M, Mecca A, O’Dell R, Van Dyck C, Carson R, Duncan J, Liu C. Synthesizing Multi-tracer PET Images for Alzheimer’s Disease Patients Using a 3D Unified Anatomy-Aware Cyclic Adversarial Network. Lecture Notes In Computer Science 2021, 12906: 34-43. DOI: 10.1007/978-3-030-87231-1_4.Peer-Reviewed Case Reports and Technical NotesGeneration of parametric Ki images for FDG PET using two 5‐min scans
Wu J, Liu H, Ye Q, Gallezot J, Naganawa M, Miao T, Lu Y, Chen M, Esserman DA, Kyriakides TC, Carson RE, Liu C. Generation of parametric Ki images for FDG PET using two 5‐min scans. Medical Physics 2021, 48: 5219-5231. PMID: 34287939, DOI: 10.1002/mp.15113.Peer-Reviewed Original ResearchConceptsPopulation-based input functionDynamic FDG-PET scansFDG-PET scansFDG-PETSUV changesPET scansClinical practiceSolid lung nodulesClinical usefulnessLate scansBone marrowRegion of interestLung nodulesInput functionScansPatlak analysisKi imagesMin/T-testCorrelation coefficientTumorsSubjectsNodulesDynamic imagingPETGeneration of synthetic PET images of synaptic density and amyloid from 18F‐FDG images using deep learning
Wang R, Liu H, Toyonaga T, Shi L, Wu J, Onofrey JA, Tsai Y, Naganawa M, Ma T, Liu Y, Chen M, Mecca AP, O’Dell R, van Dyck C, Carson RE, Liu C. Generation of synthetic PET images of synaptic density and amyloid from 18F‐FDG images using deep learning. Medical Physics 2021, 48: 5115-5129. PMID: 34224153, PMCID: PMC8455448, DOI: 10.1002/mp.15073.Peer-Reviewed Original Research
2018
Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static 18F-FDG PET as a function of injected dose
Ye Q, Wu J, Lu Y, Naganawa M, Gallezot JD, Ma T, Liu Y, Tanoue L, Detterbeck F, Blasberg J, Chen MK, Casey M, Carson RE, Liu C. Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static 18F-FDG PET as a function of injected dose. Physics In Medicine And Biology 2018, 63: 175015. PMID: 30095083, PMCID: PMC6158045, DOI: 10.1088/1361-6560/aad97f.Peer-Reviewed Original ResearchConceptsPopulation-based input functionStandardized uptake valueImage-derived input functionLung nodulesClinical trialsTime-activity curvesLow-dose computed tomography (LDCT) screeningLung cancer mortality ratesIndeterminate lung nodulesComputed Tomography ScreeningF-FDG PETCancer mortality ratesStatic PET acquisitionVirtual clinical trialsScan durationTomography screeningFDG injectionPET scansMortality rateUptake valueAccurate diagnosisMalignant lung nodulesROC analysisPatient dataMalignant nodules
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply