2023
Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model
Dong Y, Jang S, Han P, Johnson K, Ma C, Fakhri G, Li Q, Gong K. Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338188.Peer-Reviewed Original ResearchDiffusion probabilistic modelGenerative adversarial networkConditional encodingAttenuation correctionDenoising diffusion probabilistic modelLow-level featuresProbabilistic modelAttenuation coefficientAdversarial networkExtract featuresPET/MR systemsEncodingPET acquisitionNovel methodDiffusion encodingMagnetic resonanceImagesPET imagingCorrectionMR imagingUNetAttenuationNetworkFeaturesResonanceTauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models
Jang S, Gomez C, Thibault E, Becker J, Dong Y, Normandin M, Price J, Johnson K, Fakhri G, Gong K. TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338710.Peer-Reviewed Original Research
2022
A 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