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
2020
Data-Driven Motion Detection and Event-by-Event Correction for Brain PET: Comparison with Vicra
Lu Y, Naganawa M, Toyonaga T, Gallezot JD, Fontaine K, Ren S, Revilla EM, Mulnix T, Carson RE. Data-Driven Motion Detection and Event-by-Event Correction for Brain PET: Comparison with Vicra. Journal Of Nuclear Medicine 2020, 61: 1397-1403. PMID: 32005770, PMCID: PMC7456171, DOI: 10.2967/jnumed.119.235515.Peer-Reviewed Original ResearchConceptsData-driven algorithmMotion correction methodMotion tracking informationHead motionCentroid of distributionMotion-compensated reconstructionLarge head motionsMotion correction frameworkUser-defined thresholdPET raw dataDynamic datasetsTracking informationImage registrationMotion detectionRaw dataSuch time pointsImage qualityBetter performanceMotion correctionAlgorithmLine of responseCorrection frameworkBrain PET studiesCentral coordinatesTracer kinetic modeling