2024
Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising
Jang S, Pan T, Li Y, Heidari P, Chen J, Li Q, Gong K. Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising. IEEE Transactions On Medical Imaging 2024, 43: 2036-2049. PMID: 37995174, PMCID: PMC11111593, DOI: 10.1109/tmi.2023.3336237.Peer-Reviewed Original ResearchConceptsMulti-head self-attentionConvolutional neural networkSelf-attentionSignal-to-noise ratioState-of-the-art deep learning architecturesGlobal self-attentionState-of-the-artLocal feature extractionDeep learning architectureLow signal-to-noise ratioImage denoisingChannel informationChannel-wiseLearning architectureFeature extractionNeural networkTransformation frameworkComputational costReceptive fieldsImage qualityQuantitative meritDenoisingFrameworkQuantitative resultsDataset
2022
Low-Dose Tau PET Imaging Based on Swin Restormer with Diagonally Scaled Self-Attention
Jang S, Lois C, Becker J, Thibault E, Li Y, Price J, Fakhri G, Li Q, Johnson K, Gong K. Low-Dose Tau PET Imaging Based on Swin Restormer with Diagonally Scaled Self-Attention. 2022, 00: 1-3. DOI: 10.1109/nss/mic44845.2022.10399169.Peer-Reviewed Original ResearchConvolutional neural networkSelf-attention mechanismSelf-attentionTransformer architectureComputer vision tasksLocal feature extractionLong-range informationVision tasksDenoising performanceSwin TransformerFeature extractionImage datasetsUNet structureNeural networkSwinComputational costReceptive fieldsImage qualityMap calculationNetwork structureArchitecturePET image qualityChannel dimensionsQuantitative evaluationDenoisingA Noise-Level-Aware Framework for PET Image Denoising
Li Y, Cui J, Chen J, Zeng G, Wollenweber S, Jansen F, Jang S, Kim K, Gong K, Li Q. A Noise-Level-Aware Framework for PET Image Denoising. Lecture Notes In Computer Science 2022, 13587: 75-83. DOI: 10.1007/978-3-031-17247-2_8.Peer-Reviewed Original ResearchDeep convolutional neural networkPET image denoisingImage denoisingConvolutional neural networkDenoising frameworkDenoising operationBaseline methodsDenoising needsLocal noise levelBackbone networkPatient PET imagesNeural networkDenoisingNoise levelScanner sensitivityPET/CT systemNetworkPET imagingNoise-levelEmbeddingImage acquisition durationAcquisition durationAdministered activityImagesNoise