Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network.

ABSTRACT: In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET imaging. However, despite promising results in simulations and first studies, anatomically-guided PET reconstructions are not yet available for use in routine clinical because of several reasons. In light of this, we investigate whether the improvements of anatomically-guided PET reconstruction methods can be achieved entirely in the image domain with a convolutional neural network (CNN). An entirely image-based CNN post-reconstruction approach has the advantage that no access to PET raw data is needed and, moreover, that the prediction times of trained CNNs are extremely fast on state of the art GPUs which will substantially facilitate the eva

SUBMITTER: Schramm G 

PROVIDER: S-EPMC7812485 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature