Abstract #1977
Enhancing Ultra-Low-Dose PET/MRI Using Deep Learning Method for Improved Interpretation
Anum Masood1,2,3, Alexander Drzezga2,4,5, Eva-Maria Elmenhorst6,7, Anna Linea Foerges2,8, Denise Lange6, Eva Hennecke6, Diego Manuel Baur9, Tina Kroll2, Bernd Neumaier10,11,12, Daniel Aeschbach6,13,14, Andreas Bauer2,15, Hans-Peter Landolt9, David Elmenhorst2,4,16, and Simone Beer2
1Department of Radiology, Harvard Medical School, Boston Children's Hospital, Boston, MA, United States, 2Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Jülich, Germany, 3Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway, 4Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 5German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany, 6Institute of Aerospace Medicine, German Aerospace Center, Cologne, Germany, 7Institute for Occupational, Social and Environmental Medicine, RWTH Aachen University Hospital, Aachen, Germany, 8Institute of Zoology (Bio-II), Department of Neurophysiology,, RWTH Aachen University, Aachen, Germany, 9Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland, 10Institute of Neuroscience and Medicine (INM-5), Forschungszentrum Jülich, Jülich, Germany, 11Department of Nuclear Chemistry, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany, 12Institute of Radiochemistry and Experimental Molecular Imaging, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 13Division of Sleep Medicine, Harvard Medical School, Boston, MA, United States, 14Faculty of Medicine, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Cologne, Germany, 15Department of Neurology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany, 16Division of Medical Psychology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Cologne, Germany
Synopsis
Keywords: Analysis/Processing, PET/MR, Low Dose PET/MR
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Click here for more information on becoming a member.
