%0 Conference Paper
%A Masood, Anum
%A Drzezga, Alexander
%A Elmenhorst, E. M.
%A Foerges, Anna Linea
%A Lange, Denise
%A Hennecke, E.
%A Baur, D. M.
%A Kroll, Tina
%A Neumaier, Bernd
%A Aeschbach, D.
%A Bauer, Andreas
%A Landolt, H. P.
%A Elmenhorst, David
%A Beer, Simone
%T Enhancing Ultra-Low-Dose PET/MRI Using Deep Learning Method for Improved Interpretation
%M FZJ-2025-01694
%D 2024
%X Motivation: We developed a deep learning model to enhance the image quality of ultra-low dose brain PET.Goal(s): Significantly reducing the injected dose not only minimizes radiation risk in subjects but also provides options for scanning protocols, and more follow-up studies.Approach: We proposed a 3D-Residual Attention U-Net model initially trained on whole-body [18F]FDG PET/MR images. We used transfer learning approach to fine-tune our proposed model on [18F]CPFPX PET/MRI inhouse dataset.Results: We achieved improved metrics compared to U-Net model with average PSNR of 28.02 (U-Net: 21.23), SSIM of 0.81 (U-Net: 0.53), CNR of 0.72 (U-Net: 0.61) and NMSE of 0.33 (U-Net: 0.67).Impact: Our model has potential to generate high-quality PET images from low-dose PET/MR, potentially contribute to implementation of kinetic modelling using PET/MR imaging. Our model is capable of enhancing both whole-body and brain datasets, making it valuable asset for diverse applications.
%B 2024 ISMRM & ISMRT Annual Meeting & Exhibition
%C 4 May 2024 - 9 May 2024, Singapore (Singapore)
Y2 4 May 2024 - 9 May 2024
M2 Singapore, Singapore
%F PUB:(DE-HGF)24
%9 Poster
%U https://juser.fz-juelich.de/record/1038879