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
Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network
Sun J, Yang B, Li C, Du Y, Liu Y, Wu T, Mok G. Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network. Frontiers In Medicine 2023, 10: 1083413. PMID: 36817784, PMCID: PMC9935600, DOI: 10.3389/fmed.2023.1083413.Peer-Reviewed Original ResearchAttention-guided generative adversarial networkGenerative adversarial networkAdversarial networkConvolutional neural network (CNN)-based methodsDeep learning-based denoisersCNN-based networkLearning-based denoisingLocal receptive fieldsReceptive fieldsAttention mechanismConvolution kernelAdam optimizerFive-fold cross-validationAttGANAcquisition timeList mode dataJoint histogramPerfusion defect sizeCGANDefect informationUNetDenoisingNetworkMP-SPECTProjection pairs
2022
Low Dose Myocardial Perfusion SPECT Denoising Using an Attention-Based Generative Adversarial Network
Sun J, Li C, Du Y, Wu T, Yang B, Liu Y, Mok G. Low Dose Myocardial Perfusion SPECT Denoising Using an Attention-Based Generative Adversarial Network. 2022, 00: 1-3. DOI: 10.1109/nss/mic44845.2022.10399080.Peer-Reviewed Original ResearchNormalized Mean Square ErrorConvolutional neural network (CNN)-based methodsDeep learning-based denoisersConditional generative adversarial networkKernel’s receptive fieldLearning-based denoisingGenerative adversarial networkProjection-domainReceptive fieldsMean square errorList mode dataDenoising performanceAttention schemeAdversarial networkConvolution kernelAdam optimizerPerfusion defect sizeDenoisingNormalized standard deviationFull doseCGANMP-SPECTDose levelsLow dosesSquare errorDeep 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 images