TY  - CONF
AU  - Masood, Anum
AU  - Drzezga, Alexander
AU  - Elmenhorst, E. M.
AU  - Foerges, Anna Linea
AU  - Lange, Denise
AU  - Hennecke, E.
AU  - Baur, D. M.
AU  - Kroll, Tina
AU  - Neumaier, Bernd
AU  - Aeschbach, D.
AU  - Bauer, Andreas
AU  - Landolt, H. P.
AU  - Elmenhorst, David
AU  - Beer, Simone
TI  - Enhancing Ultra-Low-Dose PET/MRI Using Deep Learning Method for Improved Interpretation
M1  - FZJ-2025-01694
PY  - 2024
AB  - 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.
T2  - 2024 ISMRM & ISMRT Annual Meeting & Exhibition
CY  - 4 May 2024 - 9 May 2024, Singapore (Singapore)
Y2  - 4 May 2024 - 9 May 2024
M2  - Singapore, Singapore
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/1038879
ER  -