Takuya Toyonaga, MD, PhD
Associate Research Scientist in Radiology & Biomedical ImagingCards
About
Research
Publications
2024
Data-driven non-rigid motion detection and correction for NeuroEXPLORER
Zhang J, Sun C, Volpi T, Zeng T, Fontaine K, Du Y, Toyonaga T, Onofrey J, Lu Y, Carson R. Data-driven non-rigid motion detection and correction for NeuroEXPLORER. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10658289.Peer-Reviewed Original ResearchNon-rigid motionNon-rigid motion estimationMotion dataNon-rigid regionsHead motion dataTracking capabilityMotion estimationMotion detectionRigid transformationImage-derived input functionMotion tracking systemImage blurringCarotid arteryEffective MCMotion patternsPatient movementTracking systemMotion correction frameworkBrain PET systemRigid motionMotion-corrected reconstructionFacial surfaceRigid motion correctionCorrect reconstructionCorrection frameworkMOLAR-NX: building a PET reconstruction framework for exploring the novel features provided by the NeuroEXPLORER
Fontaine K, Gallezot J, Zhang J, He L, Gravel P, Zeng T, Li T, Li Y, Leung E, Sun X, Guo L, Mulnix T, Toyonaga T, Lu Y, Li H, Badawi R, Qi J, Carson R. MOLAR-NX: building a PET reconstruction framework for exploring the novel features provided by the NeuroEXPLORER. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10655187.Peer-Reviewed Original ResearchReconstruction processDepth of interactionReconstruction frameworkAdvanced frameworkFramework's effectivenessMask featuresNovel featuresContrast recoveryScatter correction methodReconstruction softwareFrameworkListmode dataDownsamplingMotion correctionPhantom studyListmode filesFeaturesCorrection methodSoftwareFilesNeuroExplorerReconstructionHigh-resolution brain phantom data, with flexible contrast: Validation on the NeuroExplorer (NX)
Gravel P, Toyonaga T, Gallezot J, Fontaine K, Martins S, Mulnix T, Carson R. High-resolution brain phantom data, with flexible contrast: Validation on the NeuroExplorer (NX). 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10656366.Peer-Reviewed Original ResearchQuantitative accuracyPhantom dataSpatial resolutionIterative reconstruction algorithmListmode dataAttenuation correctionPhantom studyPhantomResolution measurementsAttenuation propertiesReconstructed imagesReconstruction algorithmPET imagingCorrection accuracyResolutionScatteringAxial directionAttenuationContrastCorrectionAdaptive Deep Image Prior Enhances Ultra-Low Dose PET Imaging with NeuroEXPLORER
Li A, Gravel P, Gallezot J, Toyonaga T, Fontaine K, Carson R, Tang J. Adaptive Deep Image Prior Enhances Ultra-Low Dose PET Imaging with NeuroEXPLORER. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657691.Peer-Reviewed Original ResearchContrast recovery coefficientCounting imagingLearning-based denoising methodsHead motion correctionDeep Image PriorLow-dose imagesOptimal stopping iterationsDose imagesAttenuation mapBrain phantomDeep imagingFull-count dataImage priorsMotion correctionSignal-to-noise ratioDenoising methodSequence of outputsTraining dataPET imagingStopping iterationDecreased signal-to-noise ratioNoise ratioPost-processing techniquesReconstructed imagesRecovery coefficientGeneration of Synthetic brain PET images of synaptic density from MRI and FDG-PET using a Multi-stage U-Net
Zheng X, Worhunsky P, Liu Q, Zhou B, Chen X, Guo X, Xie H, Sun H, Zhang J, Toyonaga T, Mecca A, O’Dell R, van Dyck C, Carson R, Radhakrishnan R, Liu C. Generation of Synthetic brain PET images of synaptic density from MRI and FDG-PET using a Multi-stage U-Net. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10655600.Peer-Reviewed Original ResearchImage-Derived Input Functions on an Ultra-High Performance Brain PET Scanner: Minimizing the Carotid Partial Volume Effect
Volpi T, Zeng T, Khattar N, Toyonaga T, Martins S, Mulnix T, Fontaine K, Gallezot J, Carson R. Image-Derived Input Functions on an Ultra-High Performance Brain PET Scanner: Minimizing the Carotid Partial Volume Effect. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10658264.Peer-Reviewed Original ResearchPOUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation
Zhou B, Hou J, Chen T, Zhou Y, Chen X, Xie H, Liu Q, Guo X, Xia M, Tsai Y, Panin V, Toyonaga T, Duncan J, Liu C. POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10658051.Peer-Reviewed Original ResearchPET attenuation correctionLow-dose PETAttenuation correctionU-mapAttenuation mapElevated radiation doseRadiation doseEfficient feature extractionRadiation exposurePET imagingFinely detailed featuresBaseline methodsMitigate radiation exposureFeature extractionCorrectionMap generationGeneration machinesAn Investigation on Cross-Tracer Generalizability of Deep Learning-based PET Attenuation Correction
Hou J, Chen T, Zhou Y, Chen X, Xie H, Liu Q, Xia M, Panin V, Liu C, Zhou B, Toyonaga T. An Investigation on Cross-Tracer Generalizability of Deep Learning-based PET Attenuation Correction. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657095.Peer-Reviewed Original ResearchAttenuation correctionPET attenuation correctionQuantitative PET imagingAttenuation mapDL modelsDeep learning (DL)-based methodsTumor quantificationDL model trainingRadiation doseImmediate future workCompetitive performancePET imagingModel trainingPET signalCorrectionAnalysis of PETFuture workPreliminary resultsData availabilityRadiation11C-UCB-J PET imaging is consistent with lower synaptic density in autistic adults
Matuskey D, Yang Y, Naganawa M, Koohsari S, Toyonaga T, Gravel P, Pittman B, Torres K, Pisani L, Finn C, Cramer-Benjamin S, Herman N, Rosenthal L, Franke C, Walicki B, Esterlis I, Skosnik P, Radhakrishnan R, Wolf J, Nabulsi N, Ropchan J, Huang Y, Carson R, Naples A, McPartland J. 11C-UCB-J PET imaging is consistent with lower synaptic density in autistic adults. Molecular Psychiatry 2024, 1-7. PMID: 39367053, DOI: 10.1038/s41380-024-02776-2.Peer-Reviewed Original ResearchPositron emission tomographySynaptic densityAutistic adultsBrain regionsAutistic featuresClinical phenotype of autismNon-autistic participantsPhenotype of autismNon-autistic individualsRelationship to clinical characteristicsSynaptic vesicle glycoprotein 2AAssociated with clinical measuresPost-mortem studiesPositron emission tomography scanPrefrontal cortexClinician ratingsAutism groupNeural basisBrain areasNeural processesBetween-group differencesVolumetric differencesBinding potentialDensity of synapsesAutismValidation of a Simplified Tissue-to-Reference Ratio Measurement Using SUVR to Assess Synaptic Density Alterations in Alzheimer Disease with [11C]UCB-J PET
Young J, O’Dell R, Naganawa M, Toyonaga T, Chen M, Nabulsi N, Huang Y, Cooper E, Miller A, Lam J, Bates K, Ruan A, Nelsen K, Salardini E, Carson R, van Dyck C, Mecca A. Validation of a Simplified Tissue-to-Reference Ratio Measurement Using SUVR to Assess Synaptic Density Alterations in Alzheimer Disease with [11C]UCB-J PET. Journal Of Nuclear Medicine 2024, 65: jnumed.124.267419. PMID: 39299782, PMCID: PMC11533916, DOI: 10.2967/jnumed.124.267419.Peer-Reviewed Original ResearchDistribution volume ratioSUV ratioSynaptic densityEffect sizeAlzheimer's diseaseLongitudinal study of Alzheimer's diseaseMethods:</b> ParticipantsLongitudinal studyMeasure synaptic densityAD participantsStudy of Alzheimer's diseaseNormal cognitionReference regionOlder adultsMulticenterDensity alterations