Aspects and embodiments relate to a method of creating an image representative of a measured dataset by iteratively updating a base image. The method comprises: generating a principal dataset from the measured dataset, the principal dataset comprising a dataset having noise at substantially the same level as the measured dataset. The method comprises: generating at least one additional dataset from the measured dataset, the additional dataset comprising: a dataset generated from the measured dataset such that each additional dataset has noise at substantially the same level as the measured dataset and such that each additional dataset is not identical to the principal dataset. The method comprises: processing the base image without noise compensation using the principal dataset and each additional dataset to obtain a principal interim image and at least one additional interim image respectively； comparing the principal and the at least one additional interim image to determine an indication of a level of noise present； and using the determined indication of noise present to select noise compensation to apply when processing the base image using the measured dataset to create a new base image representative of the measured dataset. Approaches described herein may be implemented for any standard iterative image reconstruction method and provide a method in which use of, for example, just one bootstrapped resampled dataset allows powerful state-of-the-art denoising algorithms to be directly embedded into image reconstruction with relative simplicity. Approaches provide a mechanism for data-dependent automatic and precise optimisation of the strength of any denoising at every update.Des aspects et des modes de réalisation de la présente invention concernent un procédé de création d'une image représentative d'un ensemble de données mesuré par mise à jour itérative d'une image de base. Le procédé consiste à : générer un ensemble de données principal à partir de l'ensemble
