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- W4300589077 abstract "Improving the quality of positron emission tomography (PET) images, affected by low resolution and high level of noise, is a challenging task in nuclear medicine and radiotherapy. This work proposes a restoration method, achieved after tomographic reconstruction of the images and targeting clinical situations where raw data are often not accessible. Based on inverse problem methods, our contribution introduces the recently developed total generalized variation (TGV) norm to regularize PET image deconvolution. Moreover, we stabilize this procedure with additional image constraints such as positivity and photometry invariance. A criterion for updating and adjusting automatically the regularization parameter in case of Poisson noise is also presented. Experiments are conducted on both synthetic data and real patient images." @default.
- W4300589077 created "2022-10-03" @default.
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- W4300589077 date "2015-06-16" @default.
- W4300589077 modified "2023-10-18" @default.
- W4300589077 title "Post-Reconstruction Deconvolution of PET Images by Total Generalized Variation Regularization" @default.
- W4300589077 doi "https://doi.org/10.48550/arxiv.1506.04935" @default.
- W4300589077 hasPublicationYear "2015" @default.
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