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
2022
Unsupervised Diffeomorphic Registration for Even and Odd Echo Images with Applications to Point-of-Care MRI Reconstruction
Schlemper J, Dey N, Salehi S, Sheth K, Kimberly W, Cullen L, Sofka M. Unsupervised Diffeomorphic Registration for Even and Odd Echo Images with Applications to Point-of-Care MRI Reconstruction. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2022 DOI: 10.58530/2022/0699.Peer-Reviewed Original Research
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
2020
Automatic Multimodal Registration via Intraprocedural Cone-Beam CT Segmentation using MRI Distance Maps
Augenfeld Z, Lin M, Chapiro J, Duncan J. Automatic Multimodal Registration via Intraprocedural Cone-Beam CT Segmentation using MRI Distance Maps. 2020, 00: 1-4. DOI: 10.1109/isbi45749.2020.9098619.Peer-Reviewed Original ResearchRobust Point MatchingMultimodal registrationConvolutional neural networkSpatial informationDistance mapTarget image segmentationAutomatic multimodal registrationDense spatial informationMore medical imagesMedical imagesImaging domainImage segmentationSupervised fashionNeural networkTarget imageCT segmentationSegmented regionsSource imagesPoint matchingRegistration frameworkSegmentationSecond networkNetworkImage qualityImage-guided procedures
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
1999
A minimax entropy registration framework for patient setup verification in radiotherapy
Bansal R, Staib L, Chen Z, Rangarajan A, Knisely J, Nath R, Duncan J. A minimax entropy registration framework for patient setup verification in radiotherapy. Computer Assisted Surgery 1999, 4: 287-304. PMID: 10631372, DOI: 10.1002/(sici)1097-0150(1999)4:6<287::aid-igs1>3.0.co;2-9.Peer-Reviewed Original ResearchA Minimax Entropy Registration Framework for Patient Setup Verification in Radiotherapy
Bansal R, Staib L, Chen Z, Rangarajan A, Knisely J, Nath R, Duncan J. A Minimax Entropy Registration Framework for Patient Setup Verification in Radiotherapy. Computer Assisted Surgery 1999, 4: 287-304. DOI: 10.3109/10929089909148182.Peer-Reviewed Original ResearchEntropy-Based, Multiple-Portal-to-3DCT Registration for Prostate Radiotherapy Using Iteratively Estimated Segmentation
Bansal R, Staib L, Chen Z, Rangarajan A, Knisely J, Nath R, Duncan J. Entropy-Based, Multiple-Portal-to-3DCT Registration for Prostate Radiotherapy Using Iteratively Estimated Segmentation. Lecture Notes In Computer Science 1999, 1679: 567-578. DOI: 10.1007/10704282_61.Peer-Reviewed Original ResearchPatient setup verificationPortal imagesReal patient dataSingle portal imagePose parametersCT data setsRegistration frameworkRegistration parametersSetup verificationDifferent initializationsAlgorithmMultiple portalsIterative fashionData setsTransformation parametersAppropriate entropyImagesCT dataPatient dataVerificationNoise conditionsFrameworkSegmentationAccurate estimationInitialization
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