POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation
Zhou B, Hou J, Chen T, Zhou Y, Chen X, Xie H, Liu Q, Guo X, Xia M, Tsai Y, Panin V, Toyonaga T, Duncan J, Liu C. POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10658051.Peer-Reviewed Original ResearchPET attenuation correctionLow-dose PETAttenuation correctionU-mapAttenuation mapElevated radiation doseRadiation doseEfficient feature extractionRadiation exposurePET imagingFinely detailed featuresBaseline methodsMitigate radiation exposureFeature extractionCorrectionMap generationGeneration machines2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less AC
Chen T, Hou J, Xie H, Chen X, Zhou Y, Xia M, Duncan J, Liu C, Zhou B. 2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less AC. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10658551.Peer-Reviewed Original ResearchLow-dose PETStandard-dose PETImage-to-image translationPositron emission tomographyAttenuation correctionPET reconstructionOverall radiation doseCT acquisitionState-of-the-art deep learning methodsRadiation hazardRadiation doseCNN-based methodsState-of-the-artMedical image translationPatient studiesDiffusion modelDeep learning methodsHigh computation costHuman patient studiesClinical imaging toolImage translationBaseline methodsMulti-viewCNN-basedMultiple views