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
2008
Physical-Space Refraction-Corrected Transmission Ultrasound Computed Tomography Made Computationally Practical
Li S, Mueller K, Jackowski M, Dione D, Staib L. Physical-Space Refraction-Corrected Transmission Ultrasound Computed Tomography Made Computationally Practical. Lecture Notes In Computer Science 2008, 11: 280-288. PMID: 18982616, DOI: 10.1007/978-3-540-85990-1_34.Peer-Reviewed Original ResearchConceptsUltrasound Computed TomographyHigh-quality image reconstructionIterative reconstruction frameworkReconstruction frameworkReconstruction qualityComputational platformInteractive demandsTracking approachImage reconstructionConsiderable computational expenseComputational expenseCT frameworkImaged tissueFrameworkEikonal solverArchitectureTrackingPlatformProper modelingSolverComputationallyWave-front trackingCapabilityEikonal equation