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
2020
Magnetic resonance image connectivity analysis provides evidence of central nervous system mode of action for parasacral transcutaneous electro neural stimulation - A pilot study
Netto JMB, Scheinost D, Onofrey JA, Franco I. Magnetic resonance image connectivity analysis provides evidence of central nervous system mode of action for parasacral transcutaneous electro neural stimulation - A pilot study. Journal Of Pediatric Urology 2020, 16: 536-542. PMID: 32873504, DOI: 10.1016/j.jpurol.2020.08.002.Peer-Reviewed Original ResearchConceptsDorsal lateral prefrontal cortexAnterior cingulate cortexOveractive bladderFunctional connectivityPrefrontal cortexUrinary tract symptomsSacral nerve stimulatorCommon treatment modalityRight scapular regionACC functional connectivityResting-state conditionsMechanism of actionTract symptomsMotor thresholdCentral effectsACC connectivityNerve stimulatorSacral levelTreatment modalitiesFunctional connectivity dataMechanism of effectivenessAdult volunteersFrontal lobeSubcortical regionsCingulate cortexSparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation
Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annual Review Of Biomedical Engineering 2020, 22: 1-27. PMID: 32169002, PMCID: PMC9351438, DOI: 10.1146/annurev-bioeng-060418-052147.Peer-Reviewed Original Research
2018
Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning
Onofrey JA, Staib LH, Papademetris X. Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning. IEEE Transactions On Medical Imaging 2018, 38: 596-607. PMID: 30176584, PMCID: PMC6476428, DOI: 10.1109/tmi.2018.2868045.Peer-Reviewed Original Research
2015
Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients
Onofrey JA, Staib LH, Papademetris X. Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients. NeuroImage Clinical 2015, 10: 291-301. PMID: 26900569, PMCID: PMC4724039, DOI: 10.1016/j.nicl.2015.12.001.Peer-Reviewed Original ResearchSegmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration
Onofrey JA, Staib LH, Papademetris X. Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration. Lecture Notes In Computer Science 2015, 24: 662-674. PMID: 26221711, PMCID: PMC5266617, DOI: 10.1007/978-3-319-19992-4_52.Peer-Reviewed Original Research