2024
Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising
Huang Y, Liu X, Miyazaki T, Omachi S, Fakhri G, Ouyang J. Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising. 2011 IEEE Nuclear Science Symposium Conference Record 2024, 00: 1-2. PMID: 39445309, PMCID: PMC11497477, DOI: 10.1109/nss/mic/rtsd57108.2024.10655179.Peer-Reviewed Original ResearchIR tasksImage restorationImage super-resolution taskField of image restorationSuper-resolution taskLatent feature spaceConventional UNetDenoising iterationDenoising taskTransformer backboneDenoising autoencoderTexture restorationVision transformerFeature spaceAblation studiesLearning schemeBackbone networkImage generationDenoisingUNetIR modelPSNRSpatial informationAutoencoderTask
2020
High-performance rapid MR parameter mapping using model-based deep adversarial learning
Liu F, Kijowski R, Feng L, El Fakhri G. High-performance rapid MR parameter mapping using model-based deep adversarial learning. Magnetic Resonance Imaging 2020, 74: 152-160. PMID: 32980503, PMCID: PMC7669737, DOI: 10.1016/j.mri.2020.09.021.Peer-Reviewed Original ResearchConceptsConvolutional neural networkMR parameter mappingAdversarial learningState-of-the-art reconstruction methodsEnd-to-end convolutional neural networkUndersampled k-space dataConvolutional neural network approachAdversarial learning approachState-of-the-artStructural similarity indexImage reconstruction frameworkEnd-to-endImage sharpnessData consistencyConventional reconstruction approachesReconstruction approachK-space dataImprove image sharpnessImage reconstruction approachEstimated parameter mapsImage sparsityTexture restorationNetwork trainingImage datasetsReconstruction performance