2022
An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution
Sun C, Revilla EM, Zhang J, Fontaine K, Toyonaga T, Gallezot JD, Mulnix T, Onofrey JA, Carson RE, Lu Y. An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution. NeuroImage 2022, 264: 119678. PMID: 36261057, DOI: 10.1016/j.neuroimage.2022.119678.Peer-Reviewed Original ResearchConceptsMotion informationHardware-based methodsHead motion estimationPET image reconstructionMotion estimation methodData-driven methodPET raw dataHead motionMask segmentationFinal image qualityMotion estimationTracking hardwareDifferent motion estimation methodsBrain PET studiesGround truthImage reconstructionRaw dataNew algorithmObjective quality controlInaccurate motion informationImage qualityMotion correction algorithmAlgorithmMotion errorsCorrection algorithm
2019
A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation Using Deep Learning
Shi L, Onofrey J, Revilla E, Toyonaga T, Menard D, Ankrah J, Carson R, Liu C, Lu Y. A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation Using Deep Learning. Lecture Notes In Computer Science 2019, 11767: 723-731. DOI: 10.1007/978-3-030-32251-9_79.Peer-Reviewed Original ResearchAttenuation mapAttenuation correctionCT-based attenuation mapAnnihilation eventsPET attenuation correctionLine integral projectionsPET raw dataInaccurate attenuation correctionCT attenuation mapsPhysicsMaximum likelihood reconstructionAC errorsMotion resultsLikelihood reconstructionLoss functionLarge biasΜ-CT