2022
Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT
Du Y, Shang J, Sun J, Wang L, Liu YH, Xu H, Mok GSP. Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT. Journal Of Nuclear Cardiology 2022, 30: 1022-1037. PMID: 36097242, DOI: 10.1007/s12350-022-03092-4.Peer-Reviewed Original ResearchDeep LearningHumansImage Processing, Computer-AssistedPerfusionRetrospective StudiesTechnetium Tc 99m SestamibiTomography, Emission-Computed, Single-PhotonDeep 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 ResearchMeSH KeywordsAnimalsDeep LearningHumansImage Processing, Computer-AssistedPhantoms, ImagingSwineTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedConceptsDeep 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 subjectsLearningDirect and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT
Chen X, Zhou B, Xie H, Shi L, Liu H, Holler W, Lin M, Liu YH, Miller EJ, Sinusas AJ, Liu C. Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 49: 3046-3060. PMID: 35169887, PMCID: PMC9253078, DOI: 10.1007/s00259-022-05718-8.Peer-Reviewed Original Research
2021
Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation
Liu H, Wu J, Shi L, Liu Y, Miller E, Sinusas A, Liu YH, Liu C. Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation. Journal Of Nuclear Cardiology 2021, 29: 2881-2892. PMID: 34671940, DOI: 10.1007/s12350-021-02817-1.Peer-Reviewed Original Research
2020
Quantification of myocardial blood flow (MBF) and reserve (MFR) incorporated with a novel segmentation approach: Assessments of quantitative precision and the lower limit of normal MBF and MFR in patients
Liu H, Thorn S, Wu J, Fazzone-Chettiar R, Sandoval V, Miller EJ, Sinusas AJ, Liu YH. Quantification of myocardial blood flow (MBF) and reserve (MFR) incorporated with a novel segmentation approach: Assessments of quantitative precision and the lower limit of normal MBF and MFR in patients. Journal Of Nuclear Cardiology 2020, 28: 1236-1248. PMID: 32715416, DOI: 10.1007/s12350-020-02278-y.Peer-Reviewed Original ResearchDeep learning-based attenuation map generation for myocardial perfusion SPECT
Shi L, Onofrey JA, Liu H, Liu YH, Liu C. Deep learning-based attenuation map generation for myocardial perfusion SPECT. European Journal Of Nuclear Medicine And Molecular Imaging 2020, 47: 2383-2395. PMID: 32219492, DOI: 10.1007/s00259-020-04746-6.Peer-Reviewed Original ResearchMeSH KeywordsArtifactsDeep LearningHumansImage Processing, Computer-AssistedPerfusionTomography, Emission-Computed, Single-PhotonTomography, X-Ray Computed
2019
A robust segmentation method with triple‐factor non‐negative matrix factorization for myocardial blood flow quantification from dynamic 82Rb positron emission tomography
Liu H, Wu J, Sun J, Wu T, Fazzone‐Chettiar R, Thorn S, Sinusas AJ, Liu Y. A robust segmentation method with triple‐factor non‐negative matrix factorization for myocardial blood flow quantification from dynamic 82Rb positron emission tomography. Medical Physics 2019, 46: 5002-5013. PMID: 31444909, DOI: 10.1002/mp.13783.Peer-Reviewed Original ResearchCoronary CirculationFeasibility StudiesHeart VentriclesHumansImage Processing, Computer-AssistedPositron-Emission TomographyRubidium Radioisotopes
2017
A blind deconvolution method incorporated with anatomical‐based filtering for partial volume correction: Validations with 123I‐mIBG cardiac SPECT/CT
Wu J, Liu H, Zonouz T, Sandoval VM, Mohy‐ud‐Din H, Lampert RJ, Sinusas AJ, Liu C, Liu Y. A blind deconvolution method incorporated with anatomical‐based filtering for partial volume correction: Validations with 123I‐mIBG cardiac SPECT/CT. Medical Physics 2017, 44: 6435-6446. PMID: 28994458, DOI: 10.1002/mp.12622.Peer-Reviewed Original ResearchMeSH Keywords3-IodobenzylguanidineHeartHumansImage Processing, Computer-AssistedPhantoms, ImagingSignal-To-Noise RatioSingle Photon Emission Computed Tomography Computed Tomography
2013
New Approach to Quantification of Molecularly Targeted Radiotracer Uptake from Hybrid Cardiac SPECT/CT: Methodology and Validation
Li S, Sinusas AJ, Dobrucki LW, Liu YH. New Approach to Quantification of Molecularly Targeted Radiotracer Uptake from Hybrid Cardiac SPECT/CT: Methodology and Validation. Journal Of Nuclear Medicine 2013, 54: 2175-2181. PMID: 24221992, DOI: 10.2967/jnumed.113.123208.Peer-Reviewed Original Research
2007
Quantification of nuclear cardiac images: The Yale approach
Liu YH. Quantification of nuclear cardiac images: The Yale approach. Journal Of Nuclear Cardiology 2007, 14: 483-491. PMID: 17679055, DOI: 10.1016/j.nuclcard.2007.06.005.Peer-Reviewed Original Research
1999
Quantification of regional myocardial wall thickening on electrocardiogram-gated SPECT imaging
Shen M, Liu Y, Sinusas A, Fetterman R, Bruni W, Drozhinin O, Zaret B, Wackers F. Quantification of regional myocardial wall thickening on electrocardiogram-gated SPECT imaging. Journal Of Nuclear Cardiology 1999, 6: 583-595. PMID: 10608585, DOI: 10.1016/s1071-3581(99)90095-8.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsComputer SimulationDiastoleElectrocardiographyHeart VentriclesHumansImage Processing, Computer-AssistedModels, CardiovascularMyocardial InfarctionObserver VariationPhantoms, ImagingPilot ProjectsRadiopharmaceuticalsReproducibility of ResultsSoftwareSystoleTechnetium Tc 99m SestamibiTomography, Emission-Computed, Single-PhotonVentricular Function, LeftVentricular RemodelingVentriculography, First-PassConceptsPrior myocardial infarctionRegional wall thickeningNormal subjectsMyocardial infarctionWall thickeningNormal wall thickeningPilot studyRegional left ventricular functionLeft ventricular functionAbnormal wall thickeningPercent count increaseECG-gated SPECT imagesSingle photon emissionSPECT imagesCardiac cycleLeft ventricular wallReproducibility of interpretationRegional left ventricular wallPrior infarctionCoronary diseaseVentricular functionValidation studyInfarct areaNormal rangeAnatomic areasQuantification of SPECT myocardial perfusion images: Methodology and validation of the Yale-CQ method
Liu Y, Sinusas A, DeMan P, Zaret B, Wackers F. Quantification of SPECT myocardial perfusion images: Methodology and validation of the Yale-CQ method. Journal Of Nuclear Cardiology 1999, 6: 190-203. PMID: 10327104, DOI: 10.1016/s1071-3581(99)90080-6.Peer-Reviewed Original Research