Bootstrap-Optimised Regularised Image Reconstruction for Emission Tomography.

ABSTRACT: In emission tomography, iterative image reconstruction from noisy measured data usually results in noisy images, and so regularisation is often used to compensate for noise. However, in practice, an appropriate, automatic and precise specification of the strength of regularisation for image reconstruction from a given noisy measured dataset remains unresolved. Existing approaches are either empirical approximations with no guarantee of generalisation, or else are computationally intensive cross-validation methods requiring full reconstructions for a limited set of preselected regularisation strengths. In contrast, we propose a novel methodology embedded within iterative image reconstruction, using one or more bootstrapped replicates of the measured data for precise optimisation of the regu

SUBMITTER: Reader AJ 

PROVIDER: S-EPMC7273977 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature