2024
A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging
Dong S, Cai Z, Hangel G, Bogner W, Widhalm G, Huang Y, Liang Q, You C, Kumaragamage C, Fulbright R, Mahajan A, Karbasi A, Onofrey J, de Graaf R, Duncan J. A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging. Medical Image Analysis 2024, 99: 103358. PMID: 39353335, PMCID: PMC11609020, DOI: 10.1016/j.media.2024.103358.Peer-Reviewed Original ResearchDenoising diffusion modelsDeep learning-based super-resolution methodsLearning-based super-resolution methodsMulti-scale super-resolutionGenerative modelSuper-resolution methodsDeep learning modelsHigh-resolution magnetic resonance spectroscopic imagingHigh-quality imagesPost-processing approachSuper-ResolutionFlow-based networksLearning modelsLow resolutionTruncation stepLow-resolution dataSharpness adjustmentNetworkSensitivity restrictionsUncertainty estimationDiffusion modelImagesCapabilitySampling processSpectroscopic imaging
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 amountSoftwareFast-MC-PET: A Novel Deep Learning-Aided Motion Correction and Reconstruction Framework for Accelerated PET
Zhou B, Tsai Y, Zhang J, Guo X, Xie H, Chen X, Miao T, Lu Y, Duncan J, Liu C. Fast-MC-PET: A Novel Deep Learning-Aided Motion Correction and Reconstruction Framework for Accelerated PET. Lecture Notes In Computer Science 2023, 13939: 523-535. DOI: 10.1007/978-3-031-34048-2_40.Peer-Reviewed Original ResearchReconstruction frameworkMotion correctionMotion-compensated reconstructionHigh-quality imagesHigh-quality reconstruction imagesReconstruction moduleFrame reconstructionReconstruction outputMotion correction methodMotion modelingReconstructed imagesReconstruction methodImage qualityMotion typesImagesPatient motionExperimental resultsMotion-induced artifactsAcquisition dataReconstruction imagesLong acquisition timesFrameworkMultiple typesLow SNRPET acquisition
2022
Deep learning for quality assessment of optical coherence tomography angiography images
Dhodapkar RM, Li E, Nwanyanwu K, Adelman R, Krishnaswamy S, Wang JC. Deep learning for quality assessment of optical coherence tomography angiography images. Scientific Reports 2022, 12: 13775. PMID: 35962007, PMCID: PMC9374672, DOI: 10.1038/s41598-022-17709-8.Peer-Reviewed Original ResearchConceptsImage identificationDeep learning-based systemLearning-based systemNeural network classifierLow-quality imagesSupervised learning modelNeural network modelImage qualityHigh-quality imagesMachine learningNetwork classifierLearning modelGround truthNetwork modelCurve metricsOptical coherence tomography angiography (OCTA) imagesImagesSignal strengthOptical coherence tomography angiographyTomography angiography imagesAngiography imagesQuality assessmentRobust methodImageNetClassifier
2021
Use of advanced cardiac imaging in congenital heart disease: growth, indications and innovations
Steele JM, Moore RA, Lang SM. Use of advanced cardiac imaging in congenital heart disease: growth, indications and innovations. Current Opinion In Pediatrics 2021, 33: 495-502. PMID: 34374664, DOI: 10.1097/mop.0000000000001051.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsCardiac magnetic resonance imagingCardiac computed tomographyAdvanced cardiac imagingCongenital heart diseaseAdvanced imagingCHD patientsHeart diseaseCardiac imagingManagement of patientsAppropriate use criteriaLower radiation exposureMagnetic resonance imagingAugmented Reality PlatformSicker patientsComputed tomographyNoninvasive evaluationPatientsResonance imagingUse criteriaCMR techniquesRadiation exposureHigh-quality imagesReality platformDiseaseDigital technologies
2013
List-mode reconstruction for the FOCUS-220 with motion correction and spatially-variant probability density functions: Application to awake monkey imaging
Jin X, Jian Y, Mulnix T, Sandiego C, Yao R, Carson R. List-mode reconstruction for the FOCUS-220 with motion correction and spatially-variant probability density functions: Application to awake monkey imaging. 2011 IEEE Nuclear Science Symposium Conference Record 2013, 2985-2990. DOI: 10.1109/nssmic.2012.6551682.Peer-Reviewed Original ResearchEvent motion correctionIntra-frame motionMotion correctionMotion correction methodRadial offsetLarge motionEntire FOVFrame methodSpatial resolutionRadial resolutionProbability density functionCorrection methodTangential resolutionPoint sourcesMotionResolution kernelsHigh-quality imagesReconstruction algorithmList-mode reconstruction algorithmDensity functionList-mode reconstructionFOV
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply