Cross-Attention for Improved Motion Correction in Brain PET.

ABSTRACT: Head movement during long scan sessions degrades the quality of reconstruction in positron emission tomography (PET) and introduces artifacts, which limits clinical diagnosis and treatment. Recent deep learning-based motion correction work utilized raw PET list-mode data and hardware motion tracking (HMT) to learn head motion in a supervised manner. However, motion prediction results were not robust to testing subjects outside the training data domain. In this paper, we integrate a cross-attention mechanism into the supervised deep learning network to improve motion correction across test subjects. Specifically, cross-attention learns the spatial correspondence between the reference images and moving images to explicitly focus the model on the most correlative inherent information - the he

SUBMITTER: Cai Z 

PROVIDER: S-EPMC10758996 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature