Conference Presentation (After Call)

FZJ-2025-01680

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Enhancing Ultra-Low-Dose Brain PET/MRI Image Quality Using Deep Learning

Masood, A. (First author) ; Drzezga, A.FZJ* ; Elmenhorst, E. M. ; Foerges, A. L.FZJ* ; Lange, D. ; Hennecke, E. ; Baur, D. M. ; Kroll, T.FZJ* ; Neumaier, B.FZJ* ; Aeschbach, D. ; Bauer, A.FZJ* ; Landolt, H. P. ; Elmenhorst, D.FZJ* ; Beer, S. (Corresponding author)FZJ*

2024

Abstract: Aim:We developed a deep learning model to enhance the image quality of ultra-low dose brain positron emission tomography (PET). Significantly reducing the injected dose not only minimizes radiation risk in subjects but also provides options for scanning protocols, allowing e.g. for more follow-up studies. Methods:Our dataset comprises 196 brain PET/magnetic resonance (MRI) images (MPRAGE) of healthy volunteers of two sleep deprivation studies, scanned for 100 minutes each using the adenosine A1 receptor ligand [F-18]CPFPX (mean injected activity: 179.5 MBq, 10 MBq SD). From our dataset, low-dose images with Dose Reduction Factors (DRF) of 4, 10, 20, 50, and 100 were created by modifying the acquired listmode data, corresponding to injected activities of 45, 18, 9, 3.5 and 1.8 MBq.We first modified and trained a U-Net model using a dataset of 500 total-body [F-18]FDG PET/MRI images with the same DRFs as well as the corresponding full-dose images [1]. We then used a Transfer Learning approach to train the model with our data. 8 subjects were hold out for testing our model. For evaluation, we used peak signal-to-noise ratio (PSNR), Structural Similarity (SSIM), Contrast to Noise Ratio (CNR) and Normalized Mean Squared Error (NMSE).Results:Our model can synthesize enhanced low dose images that have considerably reduced noise compared to the low-dose PET image and resemble the standard-dose image. We achieved improved metrics compared to the original 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).Conclusions:Our results highlight the potential of our model for enhancing low-dose brain PET/MRI images, allowing for scans with reduced injected dose, shorter scan times or more follow-up studies. References:[1] S Xue et al., Eur J Nucl Med Mol I 49 (6), 1843 (2022).

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Contributing Institute(s):

1. Molekulare Organisation des Gehirns (INM-2)

Research Program(s):

1. 5254 - Neuroscientific Data Analytics and AI (POF4-525) (POF4-525)

Appears in the scientific report 2024

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