2024
Medical image registration via neural fields
Sun S, Han K, You C, Tang H, Kong D, Naushad J, Yan X, Ma H, Khosravi P, Duncan J, Xie X. Medical image registration via neural fields. Medical Image Analysis 2024, 97: 103249. PMID: 38963972, DOI: 10.1016/j.media.2024.103249.Peer-Reviewed Original ResearchLearning-based methodsNeural fieldsNeural networkImage registrationMedical image analysis tasksMini-batch gradient descentImage analysis tasksDeep neural networksMedical image registrationDiffeomorphic image registrationImage registration frameworkOptimization-based methodDomain shiftAnalysis tasksGradient descentCompetitive performanceImage pairsRegistration taskOptimal deformationShort computation timeRegistration frameworkDesign choicesDisplacement vector fieldComputation timeModel optimization
2021
Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration
Zhou B, Augenfeld Z, Chapiro J, Zhou SK, Liu C, Duncan JS. Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration. Medical Image Analysis 2021, 71: 102041. PMID: 33823397, PMCID: PMC8184611, DOI: 10.1016/j.media.2021.102041.Peer-Reviewed Original ResearchConceptsMultimodal registrationLiver segmentationLarge-scale manual annotationGround truthMultimodal image registrationMultimodal registration methodSegmentation networkDomain adaptationManual annotationSource modalityImage registrationRegistration frameworkSegmentationImage-guided interventionsRegistration methodMedical imagingDiagnostic medical imagingCorrect transformationLimited FOVStructure informationIntraprocedural CBCTImage qualitySegmenterExperimental resultsPatient data
2007
A frequency‐based approach to locate common structure for 2D‐3D intensity‐based registration of setup images in prostate radiotherapy
Munbodh R, Chen Z, Jaffray DA, Moseley DJ, Knisely JP, Duncan JS. A frequency‐based approach to locate common structure for 2D‐3D intensity‐based registration of setup images in prostate radiotherapy. Medical Physics 2007, 34: 3005-3017. PMID: 17822009, PMCID: PMC2796184, DOI: 10.1118/1.2745235.Peer-Reviewed Original Research
2006
Automated 2D‐3D registration of a radiograph and a cone beam CT using line‐segment enhancementa)
Munbodh R, Jaffray DA, Moseley DJ, Chen Z, Knisely JP, Cathier P, Duncan JS. Automated 2D‐3D registration of a radiograph and a cone beam CT using line‐segment enhancementa). Medical Physics 2006, 33: 1398-1411. PMID: 16752576, PMCID: PMC2796183, DOI: 10.1118/1.2192621.Peer-Reviewed Original ResearchConceptsRegistration frameworkLinear image featuresImage featuresCone-beam CT dataRigid bony structures
2003
Entropy-Based Dual-Portal-to-3-DCT Registration Incorporating Pixel Correlation
Bansal R, Staib LH, Chen Z, Rangarajan A, Knisely J, Nath R, Duncan JS. Entropy-Based Dual-Portal-to-3-DCT Registration Incorporating Pixel Correlation. IEEE Transactions On Medical Imaging 2003, 22: 29. PMID: 12703758, DOI: 10.1109/tmi.2002.806430.Peer-Reviewed Original ResearchConceptsRegistration frameworkImage dataMutual information-based registration algorithmRegistration parametersPortal imagesUltrasound image dataReal patient dataTomography image dataImage pixelsPixel correlationRegistration algorithmPatient setup verificationSegmentationPixel intensityMarkov random processInitial versionTransformation parametersAppropriate entropyImagesAlgorithmPatient dataFrameworkCT imagesLine processSetup verification