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001038879 037__ $$aFZJ-2025-01694
001038879 041__ $$aEnglish
001038879 1001_ $$0P:(DE-Juel1)200375$$aMasood, Anum$$b0$$eFirst author
001038879 1112_ $$a2024 ISMRM & ISMRT Annual Meeting & Exhibition$$cSingapore$$d2024-05-04 - 2024-05-09$$wSingapore
001038879 245__ $$aEnhancing Ultra-Low-Dose PET/MRI Using Deep Learning Method for Improved Interpretation
001038879 260__ $$c2024
001038879 3367_ $$033$$2EndNote$$aConference Paper
001038879 3367_ $$2BibTeX$$aINPROCEEDINGS
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001038879 520__ $$aMotivation: 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.
001038879 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001038879 7001_ $$0P:(DE-Juel1)177611$$aDrzezga, Alexander$$b1$$ufzj
001038879 7001_ $$0P:(DE-HGF)0$$aElmenhorst, E. M.$$b2
001038879 7001_ $$0P:(DE-Juel1)179271$$aFoerges, Anna Linea$$b3$$ufzj
001038879 7001_ $$0P:(DE-Juel1)165827$$aLange, Denise$$b4
001038879 7001_ $$0P:(DE-HGF)0$$aHennecke, E.$$b5
001038879 7001_ $$0P:(DE-HGF)0$$aBaur, D. M.$$b6
001038879 7001_ $$0P:(DE-Juel1)131691$$aKroll, Tina$$b7$$ufzj
001038879 7001_ $$0P:(DE-Juel1)166419$$aNeumaier, Bernd$$b8$$ufzj
001038879 7001_ $$0P:(DE-HGF)0$$aAeschbach, D.$$b9
001038879 7001_ $$0P:(DE-Juel1)131672$$aBauer, Andreas$$b10$$ufzj
001038879 7001_ $$0P:(DE-HGF)0$$aLandolt, H. P.$$b11
001038879 7001_ $$0P:(DE-Juel1)131679$$aElmenhorst, David$$b12$$ufzj
001038879 7001_ $$0P:(DE-Juel1)133864$$aBeer, Simone$$b13$$eLast author$$ufzj
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001038879 9141_ $$y2024
001038879 920__ $$lyes
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