Una red Transformer de modalidad cruzada para la eliminación de ruido de imágenes PET de Tau en dosis bajas guiada...

Tau PET imaging is an essential imaging modality for the diagnosis and monitoring of Alzheimer's disease and related dementias. To enable tau PET imaging-based longitudinal monitoring of disease progression, further reducing the injected dose during each scan is important. In this work, we developed a novel deep learning approach that incorporated cross-modality transformer blocks to integrate both PET and MR prior information to further improve low-dose tau PET imaging. Both spatial and channel information were utilized during the calculation of cross-modality self-attention maps. Performance of the proposed method was evaluated based on the early-frame and late-frame images from 139 dynamic ¹⁸F-MK-6240 tau PET datasets. Results showed that the proposed network can outperform other reference networks which concatenated PET and MR images together as the network input.