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 Few-Angle Cardiac SPECT Reconstruction Using Transformer
Xie H, Thorn S, Liu Y, Lee S, Liu Z, Wang G, Sinusas A, Liu C. Deep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer. IEEE Transactions On Radiation And Plasma Medical Sciences 2022, 7: 33-40. PMID: 37397179, PMCID: PMC10312390, DOI: 10.1109/trpms.2022.3187595.Peer-Reviewed Original ResearchConvolutional neural networkLimitations of CNNMedical imaging tasksDeep U-NetImage reconstruction taskCardiac SPECT imagesComputer visionVision TransformerConvolutional kernelsTransformer networkAttention blockInput imageU-NetNeural networkMemory burdenImage sizeInductive biasInformative featuresImage volumesImaging tasksTesting dataNetworkWhole 3D volumeNetwork structureCardiac single photon emissionIncreasing 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
2021
Impaired Myocardial Flow Reserve on 82Rubidium Positron Emission Tomography/Computed Tomography in Patients With Systemic Sclerosis.
Feher A, Boutagy NE, Oikonomou EK, Thorn S, Liu YH, Miller EJ, Sinusas AJ, Hinchcliff M. Impaired Myocardial Flow Reserve on 82Rubidium Positron Emission Tomography/Computed Tomography in Patients With Systemic Sclerosis. The Journal Of Rheumatology 2021, 48: 1574-1582. PMID: 34266986, PMCID: PMC10275580, DOI: 10.3899/jrheum.210040.Peer-Reviewed Original ResearchConceptsReduced Myocardial Flow ReserveMyocardial flow reserveRaynaud's phenomenonPrimary Raynaud's phenomenonSecondary Raynaud's phenomenonPatient controlsHealthy participantsFlow reservePositron Emission Tomography/Computed TomographyEmission Tomography/Computed TomographyTomography/Computed TomographyPositron emission tomography/Impaired Myocardial Flow ReserveCoronary microvascular dysfunctionLarge prospective studiesMultivariable logistic regressionEmission tomography/PET/CTSSc-RPMicrovascular dysfunctionClinical predictorsIndependent predictorsSystemic sclerosisPrognostic valueProspective study
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 Research
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 Research
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