2023
DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT
Chen X, Zhou B, Xie H, Guo X, Zhang J, Duncan J, Miller E, Sinusas A, Onofrey J, Liu C. DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT. Medical Image Analysis 2023, 88: 102840. PMID: 37216735, PMCID: PMC10524650, DOI: 10.1016/j.media.2023.102840.Peer-Reviewed Original ResearchMeSH KeywordsHeartHumansImage Processing, Computer-AssistedPhantoms, ImagingTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedConceptsCross-modality registrationConvolutional layersCo-attention mechanismMultiple convolutional layersCo-attention moduleDifferent convolutional layersMedical image registrationInput data streamDeep learning strategiesLow registration errorIntensity-based registration methodCardiac SPECTΜ-mapsDeep learningFeature fusionData streamsInput imageSource codeFeature mapsNeural networkImage registrationSpatial featuresRegistration performanceRegistration methodInput informationDeep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects
Tram N, Chou T, Janse S, Bobbey A, Audino A, Onofrey J, Stacy M. Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects. European Radiology 2023, 33: 6599-6607. PMID: 36988714, DOI: 10.1007/s00330-023-09587-z.Peer-Reviewed Original ResearchConceptsProportional hazards regression analysisHazards regression analysisLate effectsBody composition measuresAYA patientsHigh riskBody compositionCox proportional hazards regression analysisTreatment-related late effectsComposition measuresCancer treatmentSerious adverse eventsLate treatment effectsYoung adult patientsSubcutaneous adipose tissueRegression analysisCare CT imagesSingle-site studyMuscle tissueAdult patientsAdverse eventsInitial stagingPediatric patientsAdult lymphomasPrognostic value
2020
Deep learning-based attenuation map generation for myocardial perfusion SPECT
Shi L, Onofrey JA, Liu H, Liu YH, Liu C. Deep learning-based attenuation map generation for myocardial perfusion SPECT. European Journal Of Nuclear Medicine And Molecular Imaging 2020, 47: 2383-2395. PMID: 32219492, DOI: 10.1007/s00259-020-04746-6.Peer-Reviewed Original ResearchMeSH KeywordsArtifactsDeep LearningHumansImage Processing, Computer-AssistedPerfusionTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedSparse 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 ResearchMeSH KeywordsArtifactsBrainDeep LearningHumansImage Processing, Computer-AssistedSurgery, Computer-AssistedTomography, X-Ray Computed
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
2013
Learning Nonrigid Deformations for Constrained Multi-modal Image Registration
Onofrey JA, Staib LH, Papademetris X. Learning Nonrigid Deformations for Constrained Multi-modal Image Registration. Lecture Notes In Computer Science 2013, 16: 171-178. PMID: 24505758, PMCID: PMC4044829, DOI: 10.1007/978-3-642-40760-4_22.Peer-Reviewed Original Research