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• W2904356739 abstract "In this paper, a new generic regularized reconstruction framework based on confidence interval constraints for tomographic reconstruction is presented. As opposed to usual state-of-the-art regularization methods that try to minimize a cost function expressed as the sum of a data-fitting term and a regularization term weighted by a scalar parameter, the proposed algorithm is a two-step process. The first step concentrates on finding a set of images that rely on the direct estimation of confidence intervals for each reconstructed value. Then, the second step uses confidence intervals as a constraint to choose the most appropriate candidate according to a regularization criterion. Two different constraints are proposed in this paper. The first one has the main advantage of strictly ensuring that the regularized solution will respect the interval-valued data-fitting constraint, thus preventing over-smoothing of the solution while offering interesting properties in terms of spatial and statistical bias/variance trade-off. Another regularization proposition based on the design of a smoother constraint also with appealing properties is proposed as an alternative. The competitiveness of the proposed framework is illustrated in comparison to other regularization schemes using analytical and GATE-based simulation and real PET acquisition." @default.
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• W2904356739 date "2019-06-01" @default.
• W2904356739 modified "2023-10-16" @default.
• W2904356739 title "Confidence Interval Constraint-Based Regularization Framework for PET Quantization" @default.
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• W2904356739 doi "https://doi.org/10.1109/tmi.2018.2886431" @default.
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• W2904356739 hasPublicationYear "2019" @default.
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