2024
CT-Less Whole-Body Bone Segmentation of PET Images Using a Multimodal Deep Learning Network
Bao N, Zhang J, Li Z, Wei S, Zhang J, Greenwald S, Onofrey J, Lu Y, Xu L. CT-Less Whole-Body Bone Segmentation of PET Images Using a Multimodal Deep Learning Network. IEEE Journal Of Biomedical And Health Informatics 2024, PP: 1-16. DOI: 10.1109/jbhi.2024.3501386.Peer-Reviewed Original ResearchPositron emission tomographyMultimodal fusion modulePositron emission tomography imagingMultimodal fusion networkAttenuation mapDice similarity coefficientFusion moduleFusion networkEncoder RepresentationsEncoder branchesCT imagesTraining dataComputed tomographyTumor analysisTracer activityModality imagesMultimodal deep learning networkPET imagingBone segmentsSqueeze-and-excitationBone cancerPositron emission tomography informationCT-based approachDeep learning networkImprove segmentation performance
2021
Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images
Pak D, Liu M, Ahn S, Caballero A, Onofrey J, Liang L, Sun W, Duncan J. Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images. Lecture Notes In Computer Science 2021, 12729: 637-648. DOI: 10.1007/978-3-030-78191-0_49.Peer-Reviewed Original ResearchSupervised deep learningTranscather aortic valve replacementDeep learningSegmentation labelsMesh generationCorrespondence accuracyHeavy assumptionsFinite element mesh generationMesh topologyVolumetric meshLow contrastSignificant bottleneckValve modelingProblem formulationPrediction modelModel performanceCT imagesDeformation strategyLarge amountImagesBottleneckLearningMeshFrameworkLabels
2018
Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data
Lu Y, Fontaine K, Mulnix T, Onofrey JA, Ren S, Panin V, Jones J, Casey ME, Barnett R, Kench P, Fulton R, Carson RE, Liu C. Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data. Journal Of Nuclear Medicine 2018, 59: 1480-1486. PMID: 29439015, PMCID: PMC6126443, DOI: 10.2967/jnumed.117.203000.Peer-Reviewed Original ResearchConceptsMotion correction frameworkMotion informationReference gatePET reconstructionMotion estimation accuracyGated PET dataMotion compensation approachMotion correctionMotion compensation methodMotion estimationRespiratory motion compensationAttenuation correction artifactsLung cancer datasetMotion compensationCT imagesNAC approachReconstruction algorithmPET dataPET imagesNew frameworkInaccurate localizationCancer datasetsBreathing variationsAttenuation correction mapsHuman datasets
2015
Segmenting 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