2025
Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning
Zheng X, Worhunsky P, Liu Q, Guo X, Chen X, Sun H, Zhang J, Toyonaga T, Mecca A, O’Dell R, van Dyck C, Angarita G, Cosgrove K, D’Souza D, Matuskey D, Esterlis I, Carson R, Radhakrishnan R, Liu C. Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning. EJNMMI Physics 2025, 12: 30. PMID: 40163154, PMCID: PMC11958861, DOI: 10.1186/s40658-025-00744-5.Peer-Reviewed Original ResearchCannabis use disorderStructural similarity indexPET imagingImages of higher qualityMR-T1 imagesMean square errorUse disorderEncoder-decoderDeep learningCross-validation processData-driven approachDiagnostic categoriesLow-dose scansPredicted imageTemporal regionsBrain disordersGround truthT1-weighted MRISynaptic densityHuman brainSimilarity indexDisordersSevere neurological disordersTranslation accuracyNoise reduction
2024
Molecular MRI of T-cell immune response to cryoablation in immunologically hot vs. cold hepatocellular carcinoma
Santana J, Shewarega A, Nam D, Duncan J, Madoff D, Hyder F, Coman D, Chapiro J. Molecular MRI of T-cell immune response to cryoablation in immunologically hot vs. cold hepatocellular carcinoma. JHEP Reports 2024, 7: 101294. PMID: 40028344, PMCID: PMC11870164, DOI: 10.1016/j.jhepr.2024.101294.Peer-Reviewed Original ResearchT cell infiltrationHepatocellular carcinomaRadiological-pathological correlationImaging mass cytometryImmune responseT1-weighted MRITumor-infiltrating CD8+ T lymphocytesAnti-tumor immune responseCD8+ T lymphocytesIncreased T lymphocyte infiltrationImaging biomarkersNon-immunogenic tumorsSystemic lymph nodesT lymphocyte infiltrationMurine tumor modelsImmune cell typesLocal tumor therapyPrimary liver cancerNon-invasive imaging biomarkerTesla MRI scannerInduce liver cirrhosisImmunogenic tumorsLocoregional therapySystemic immunotherapyHCC lesionsAlterations in Volume and Intrinsic Resting-State Functional Connectivity Detected at Brain MRI in Individuals with Opioid Use Disorder.
Mehta S, Peterson H, Ye J, Ibrahim A, Saeed G, Linsky S, Kreinin I, Tsang S, Nwanaji-Enwerem U, Raso A, Arora J, Tokoglu F, Yip S, Hahn C, Lacadie C, Greene A, Jeon S, Constable R, Barry D, Redeker N, Yaggi H, Scheinost D. Alterations in Volume and Intrinsic Resting-State Functional Connectivity Detected at Brain MRI in Individuals with Opioid Use Disorder. Radiology 2024, 313: e240514. PMID: 39656127, PMCID: PMC11694074, DOI: 10.1148/radiol.240514.Peer-Reviewed Original ResearchConceptsHealthy control participantsRight medial temporal lobeOpioid use disorderFunctional brain alterationsMedial temporal lobeOpioid use disorder groupFunctional connectivityUse disorderControl participantsBrain alterationsIntrinsic resting-state functional connectivityTemporal lobeMedial prefrontal cortex volumesVoxel-wise linear regressionT1-weighted MRIResting-state functional connectivityFamily-wise error correctionPrefrontal cortex volumeResting-state functional MRIIncreased functional connectivityIntrinsic connectivity distributionFunctional MRI studiesFemale participantsRegional brain volumesAssess group differences
2013
Transcriptomics of cortical gray matter thickness decline during normal aging
Kochunov P, Charlesworth J, Winkler A, Hong LE, Nichols TE, Curran JE, Sprooten E, Jahanshad N, Thompson PM, Johnson MP, Kent JW, Landman BA, Mitchell B, Cole SA, Dyer TD, Moses EK, Goring HH, Almasy L, Duggirala R, Olvera RL, Glahn DC, Blangero J. Transcriptomics of cortical gray matter thickness decline during normal aging. NeuroImage 2013, 82: 273-283. PMID: 23707588, PMCID: PMC3759649, DOI: 10.1016/j.neuroimage.2013.05.066.Peer-Reviewed Original ResearchConceptsPathway enrichment analysisTranscriptome dataInnate immune pathwaysEnrichment analysisCellular proliferationInnate immune response pathwaysImmune pathwaysImmune response pathwaysResponse pathwaysFunctional categoriesPathway enrichmentTranscriptional activityGenesExpression activityTranscriptsHigh-resolution T1-weighted MRINormal cerebral agingCommunity-dwelling membersPathwaySignificance thresholdRegenerative capacityT1-weighted MRIDifferentiationCerebral agingViral infection
2007
A Quantitative and Reproducible Method to Assess Cord Compression and Canal Stenosis After Cervical Spine Trauma
Furlan JC, Fehlings MG, Massicotte EM, Aarabi B, Vaccaro AR, Bono CM, Madrazo I, Villanueva C, Grauer JN, Mikulis D. A Quantitative and Reproducible Method to Assess Cord Compression and Canal Stenosis After Cervical Spine Trauma. Spine 2007, 32: 2083-2091. PMID: 17762809, DOI: 10.1097/brs.0b013e318145a91c.Peer-Reviewed Original ResearchMeSH KeywordsAcute DiseaseAdultAgedCervical VertebraeFemaleHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedNorth AmericaObserver VariationPrognosisReproducibility of ResultsSeverity of Illness IndexSignal Processing, Computer-AssistedSpinal Cord CompressionSpinal Cord InjuriesSpinal StenosisTomography, X-Ray ComputedConceptsMaximum spinal cord compressionTraumatic cervical spinal cord injuryCervical spinal cord injuryMaximum canal compromiseSpinal cord injuryInterclass correlation coefficientCord compressionIntrarater reliabilityAcute traumatic cervical spinal cord injuryTraumatic spinal cord injuryCervical spine traumaSpinal cord compressionT1-weighted MRIT2-weighted MR imagesCanal stenosisCanal compromiseSpine traumaPrognostic valueCord injuryRadiologic parametersCervical spineBACKGROUND DATACT scanMidsagittal MRIInterrater ICC
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