Version 1
: Received: 13 March 2024 / Approved: 13 March 2024 / Online: 14 March 2024 (12:26:34 CET)
How to cite:
Mahmoodian, N.; Rezapourian, M.; Inamdar, A.A.; Kumar, K.; Fachet, M.; Hoeschen, C. Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep Learning-Based Denoising. Preprints2024, 2024030862. https://doi.org/10.20944/preprints202403.0862.v1
Mahmoodian, N.; Rezapourian, M.; Inamdar, A.A.; Kumar, K.; Fachet, M.; Hoeschen, C. Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep Learning-Based Denoising. Preprints 2024, 2024030862. https://doi.org/10.20944/preprints202403.0862.v1
Mahmoodian, N.; Rezapourian, M.; Inamdar, A.A.; Kumar, K.; Fachet, M.; Hoeschen, C. Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep Learning-Based Denoising. Preprints2024, 2024030862. https://doi.org/10.20944/preprints202403.0862.v1
APA Style
Mahmoodian, N., Rezapourian, M., Inamdar, A.A., Kumar, K., Fachet, M., & Hoeschen, C. (2024). Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep Learning-Based Denoising. Preprints. https://doi.org/10.20944/preprints202403.0862.v1
Chicago/Turabian Style
Mahmoodian, N., Melanie Fachet and Christoph Hoeschen. 2024 "Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep Learning-Based Denoising" Preprints. https://doi.org/10.20944/preprints202403.0862.v1
Abstract
X-ray Fluorescence Computed Tomography (XFCT) is an emerging non-invasive imaging technique providing molecular-level data, gaining attention for high-resolution imaging. However, increased sensitivity with benchtop X-ray sources raises radiation exposure. Artificial Intelligence (AI), particularly deep learning (DL), has revolutionized medical imaging by delivering high-quality images in the presence of noise. In XFCT, traditional methods rely on complex algorithms for noise reduction, but AI holds promise in addressing high-dose concerns. We present an optimized SCUNet model for noise reduction in low-concentration XRF images. Compared to higher-dose images, our method’s effectiveness is evaluated. While various denoising techniques exist for X-ray and CT, few address to XFCT. The DL model is trained and assessed using the augmented data, focusing on background noise reduction. Image quality is measured using PSNR and SSIM, comparing outcomes with 100% X-ray dose images. Results show the proposed algorithm achieves high-quality images from low-dose and low-contrast agents, with a maximum PSNR of 39.05 and SSIM of 0.86. The model outperforms BM3D, NLM, DnCNN, and SCUNet in both visual inspection and quantitative analysis, particularly in high-noise scenarios. This indicates the potential of AI, specifically the SCUNet model, in significantly improving XFCT imaging by mitigating the trade-off between sensitivity and radiation exposure.
Keywords
Deep learning; Artificial Intelligence; Xray Fluorescence; XFCT; AI; DL
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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