2023
DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT
Chen X, Zhou B, Xie H, Guo X, Zhang J, Duncan J, Miller E, Sinusas A, Onofrey J, Liu C. DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT. Medical Image Analysis 2023, 88: 102840. PMID: 37216735, PMCID: PMC10524650, DOI: 10.1016/j.media.2023.102840.Peer-Reviewed Original ResearchConceptsCross-modality registrationConvolutional layersCo-attention mechanismMultiple convolutional layersCo-attention moduleDifferent convolutional layersMedical image registrationInput data streamDeep learning strategiesLow registration errorIntensity-based registration methodCardiac SPECTΜ-mapsDeep learningFeature fusionData streamsInput imageSource codeFeature mapsNeural networkImage registrationSpatial featuresRegistration performanceRegistration methodInput information
2022
High-Resolution and High-Sensitivity Spread Field Imaging to Differentiate Sub-endocardium and Sub-epicardium in Cardiac SPECT — A Preliminary Phantom Study
Mu Z, Palyo R, Goyal D, Sandoval V, Mu Z, Tao Z, Sinusas A, Miller E, Liu Y. High-Resolution and High-Sensitivity Spread Field Imaging to Differentiate Sub-endocardium and Sub-epicardium in Cardiac SPECT — A Preliminary Phantom Study. 2022, 00: 1-4. DOI: 10.1109/nss/mic44845.2022.10399016.Peer-Reviewed Original ResearchTc-99m solutionHole imagesCardiac SPECTCardiac phantomMyocardial perfusion defectsField imagesParallel hole collimatorResolution of SPECTSub-endocardiumSubset expectation maximizationCZT detectorsSub-epicardiumHole collimatorPerfusion defectsHigh-resolutionOSEM algorithmCollimatorPhantom studyScanner configurationReconstruction technologyPhantomSPECT slicesTransmural perfusion gradientExpectation maximizationOSEM
2021
Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep Learning for Cardiac SPECT: A Feasibility Study
Torkaman M, Yang J, Shi L, Wang R, Miller EJ, Sinusas AJ, Liu C, Gullberg GT, Seo Y. Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep Learning for Cardiac SPECT: A Feasibility Study. IEEE Transactions On Radiation And Plasma Medical Sciences 2021, 6: 755-765. PMID: 36059429, PMCID: PMC9438341, DOI: 10.1109/trpms.2021.3138372.Peer-Reviewed Original ResearchData management strategiesTraining dataAdvanced networksDeep learning techniquesConventional U-NetRepresentation of dataSimilarity of dataDeep learningLearning techniquesGAN networkData managementDL modelsU-NetPerformance variabilityNetworkDimensional spaceAttenuation correctionEffective trainingCardiac SPECTGlobal performanceImagesTaskLearningTrainingSpace