2024
TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Guo X, Shi L, Chen X, Liu Q, Zhou B, Xie H, Liu Y, Palyo R, Miller E, Sinusas A, Staib L, Spottiswoode B, Liu C, Dvornek N. TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction. Medical Image Analysis 2024, 96: 103190. PMID: 38820677, PMCID: PMC11180595, DOI: 10.1016/j.media.2024.103190.Peer-Reviewed Original ResearchGenerative adversarial networkAdversarial networkMotion estimation accuracyInter-frame motionIntensity-based image registration techniqueAll-to-oneSegmentation masksImage registration techniquesOriginal frameTemporal informationDiagnosis accuracyMyocardial blood flowEstimation accuracyFrame conversionPositron emission tomographyNovel methodImage qualityPET datasetsRegistration techniqueNetworkCardiac positron emission tomographyBlood flowDynamic cardiac positron emission tomographyMotion correctionCoronary artery disease
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
2022
Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT
Sun J, Jiang H, Du Y, Li C, Wu T, Liu Y, Yang B, Mok G. Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT. Journal Of Nuclear Cardiology 2022, 30: 970-985. PMID: 35982208, DOI: 10.1007/s12350-022-03045-x.Peer-Reviewed Original ResearchConceptsConditional generative adversarial networkGenerative adversarial networkImage qualityAdversarial networkOS-EM methodList-mode dataXCAT phantomPost-reconstruction filteringImagesSPECT projectionsDenoisingMyocardial perfusion SPECTHigh noise levelsPerfusion SPECTFull doseSPECT/CT scansNetworkDifferent anatomical variationsMode dataFilteringMP-SPECTLD imagesIncreasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction
Xie H, Thorn S, Chen X, Zhou B, Liu H, Liu Z, Lee S, Wang G, Liu YH, Sinusas AJ, Liu C. Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction. Journal Of Nuclear Cardiology 2022, 30: 86-100. PMID: 35508796, DOI: 10.1007/s12350-022-02972-z.Peer-Reviewed Original ResearchConceptsDeep learningReconstruction qualityImage reconstructionDeep learning methodsDeep neural networksDeep learning resultsImage qualityNetwork trainingSPECT image reconstructionNeural networkLearning methodsHigh image resolutionImage volumesClinical softwareImage metricsImage resolutionReconstruction resultsImproved image qualityTesting dataLearning resultsNetwork resultsPhysical phantomStationary imagingDifferent subjectsLearning
2016
SPECT Imaging of 2-D and 3-D Distributed Sources with Near-Field Coded Aperture Collimation: Computer Simulation and Real Data Validation
Mu Z, Dobrucki LW, Liu YH. SPECT Imaging of 2-D and 3-D Distributed Sources with Near-Field Coded Aperture Collimation: Computer Simulation and Real Data Validation. Journal Of Medical And Biological Engineering 2016, 36: 32-43. PMID: 27069461, PMCID: PMC4791458, DOI: 10.1007/s40846-016-0111-6.Peer-Reviewed Original ResearchMaximum likelihood expectation maximizationReconstruction approachImage resolutionLarge projection angleIterative image reconstructionNoise artifactsMicro-SPECT systemComputer simulationsReal data validationImage reconstructionDigital phantomExpectation maximizationData validationCA moduleMLEM algorithmImage qualityPinhole imagesProjection angleCommercial softwareImagesPhantom imagesSquared errorSPECT imagesCA imagesImage contrast
1998
A novel geometry for SPECT imaging associated with the EM-type blind deconvolution method
Liu Y, Rangarajan A, Gagnon D, Therrien M, Sinusas A, Wackers F, Zubal I. A novel geometry for SPECT imaging associated with the EM-type blind deconvolution method. IEEE Transactions On Nuclear Science 1998, 45: 2095-2101. DOI: 10.1109/23.708310.Peer-Reviewed Original ResearchBlind deconvolution algorithmImage qualityHand dataPhantom dataSPECT projectionsBlind deconvolution methodAperture projectionsDeconvolution algorithmImage resolutionAlgorithmDual-head SPECT systemParallel-hole collimatorDual-head SPECTImaging systemSystemCollimator headHole collimatorSPECT systemRestoration processImagesAperture collimatorNoise ratioCount sensitivityDataQuality
1997
A novel geometry for SPECT imaging associated with the EM-type blind deconvolution method
Liu Y, Rangarajan A, Gagnon D, Therrien M, Sinusas A, Wackers F, Zubal I. A novel geometry for SPECT imaging associated with the EM-type blind deconvolution method. 2011 IEEE Nuclear Science Symposium Conference Record 1997, 2: 1014-1017 vol.2. DOI: 10.1109/nssmic.1997.670482.Peer-Reviewed Original ResearchBlind deconvolution algorithmHigh-resolution projection imagesImage qualitySPECT imaging systemHand dataProjection imagesBlind deconvolution methodDecoding techniquesAperture projectionsImaging systemCamera headDeconvolution algorithmImage resolutionAlgorithmPhantom dataImagesSPECT projectionsParallel-hole collimatorDual-head SPECTHole collimatorSystemCollimator headRestoration processRod phantomAperture collimator