2024
Adaptive Correspondence Scoring for Unsupervised Medical Image Registration
Zhang X, Stendahl J, Staib L, Sinusas A, Wong A, Duncan J. Adaptive Correspondence Scoring for Unsupervised Medical Image Registration. Lecture Notes In Computer Science 2024, 15096: 76-92. DOI: 10.1007/978-3-031-72920-1_5.Peer-Reviewed Original ResearchMedical image registrationAdaptation frameworkMedical image datasetsUnsupervised learning schemeAdaptive training schemeImage registrationError residualsSupervision signalsLearning schemeImage datasetsRegistration architectureIntensity constancyScore mapNoisy gradientsMedical imagesTraining schemeImage reconstructionPerformance degradationLambertian assumptionCorrespondence scoresLoss of correspondenceTraining objectivesDisplacement estimationImage acquisitionScheme
2023
Transformer-Based Dual-Domain Network for Few-View Dedicated Cardiac SPECT Image Reconstructions
Xie H, Zhou B, Chen X, Guo X, Thorn S, Liu Y, Wang G, Sinusas A, Liu C. Transformer-Based Dual-Domain Network for Few-View Dedicated Cardiac SPECT Image Reconstructions. Lecture Notes In Computer Science 2023, 14229: 163-172. DOI: 10.1007/978-3-031-43999-5_16.Peer-Reviewed Original ResearchDual-domain networkSPECT image reconstructionImage reconstructionDeep learning methodsPrevious baseline methodsCardiac SPECT imagesHigh-quality imagesReconstruction networkIterative reconstruction processView reconstructionBaseline methodsReconstruction outputLearning methodsClinical softwareReconstruction processImaging problemsProjection dataImage qualityNetworkImagesStationary dataSPECT scannerDiagnosis of CVDLimited amountSoftware
2015
Simultaneous CT-MRI Reconstruction for Constrained Imaging Geometries Using Structural Coupling and Compressive Sensing
Xi Y, Zhao J, Bennett J, Stacy M, Sinusas A, Wang G. Simultaneous CT-MRI Reconstruction for Constrained Imaging Geometries Using Structural Coupling and Compressive Sensing. IEEE Transactions On Biomedical Engineering 2015, 63: 1301-1309. PMID: 26672028, PMCID: PMC4930897, DOI: 10.1109/tbme.2015.2487779.Peer-Reviewed Original ResearchConceptsMRI image reconstructionImage estimation methodCompressive sensing techniquesUnified reconstruction frameworkNovel research directionsMRI reconstructionReconstruction frameworkCompressive sensingImage reconstructionPrior knowledgeDifferent modalitiesReconstruction methodologyResearch directionsInstantaneous acquisitionMRI dataPromising resultsImaging geometrySeparate reconstructionBetter CTNumerical phantomImproved resultsMRI acquisitionSensing techniquesEstimation methodApplications