Hauptseite > Publikationsdatenbank > Enhancing Ultra-Low-Dose PET/MRI Using Deep Learning Method for Improved Interpretation > print

001     1038879
005     20250220092005.0
037 _ _ |a FZJ-2025-01694
041 _ _ |a English
100 1 _ |a Masood, Anum
|0 P:(DE-Juel1)200375
|b 0
|e First author
111 2 _ |a 2024 ISMRM & ISMRT Annual Meeting & Exhibition
|c Singapore
|d 2024-05-04 - 2024-05-09
|w Singapore
245 _ _ |a Enhancing Ultra-Low-Dose PET/MRI Using Deep Learning Method for Improved Interpretation
260 _ _ |c 2024
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a CONFERENCE_POSTER
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336 7 _ |a Output Types/Conference Poster
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336 7 _ |a Poster
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|0 PUB:(DE-HGF)24
|s 1739281731_3184
|2 PUB:(DE-HGF)
|x After Call
520 _ _ |a 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.
536 _ _ |a 5254 - Neuroscientific Data Analytics and AI (POF4-525)
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700 1 _ |a Drzezga, Alexander
|0 P:(DE-Juel1)177611
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700 1 _ |a Elmenhorst, E. M.
|0 P:(DE-HGF)0
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700 1 _ |a Foerges, Anna Linea
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700 1 _ |a Lange, Denise
|0 P:(DE-Juel1)165827
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700 1 _ |a Hennecke, E.
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700 1 _ |a Baur, D. M.
|0 P:(DE-HGF)0
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700 1 _ |a Kroll, Tina
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700 1 _ |a Neumaier, Bernd
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700 1 _ |a Aeschbach, D.
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700 1 _ |a Bauer, Andreas
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700 1 _ |a Landolt, H. P.
|0 P:(DE-HGF)0
|b 11
700 1 _ |a Elmenhorst, David
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700 1 _ |a Beer, Simone
|0 P:(DE-Juel1)133864
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|e Last author
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909 C O |o oai:juser.fz-juelich.de:1038879
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913 1 _ |a DE-HGF
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|v Decoding Brain Organization and Dysfunction
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914 1 _ |y 2024
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-2-20090406
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980 _ _ |a poster
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-2-20090406
980 _ _ |a UNRESTRICTED

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