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J. Imaging
Volume 10
Issue 6
10.3390/jimaging10060127
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Open AccessArticle
by Naghmeh Mahmoodian SciProfiles Scilit Preprints.org Google Scholar Mohammad Rezapourian SciProfiles Scilit Preprints.org Google Scholar Asim Abdulsamad Inamdar SciProfiles Scilit Preprints.org Google Scholar Kunal Kumar SciProfiles Scilit Preprints.org Google Scholar Melanie Fachet SciProfiles Scilit Preprints.org Google Scholar Christoph Hoeschen SciProfiles Scilit Preprints.org Google Scholar Naghmeh Mahmoodian
,
Mohammad Rezapourian
,
Asim Abdulsamad Inamdar
Kunal Kumar
,
Melanie Fachet
Christoph Hoeschen
Chair of Medical Systems Technology, Institute for Medical Technology, Faculty of Electrical Engineering and Information Technology, Otto von Guericke University, 39106 Magdeburg, Germany
*
Author to whom correspondence should be addressed.
J. Imaging 2024, 10(6), 127; https://doi.org/10.3390/jimaging10060127
Submission received: 8 March 2024 / Revised: 10 May 2024 / Accepted: 18 May 2024 / Published: 22 May 2024
(This article belongs to the Special Issue Recent Advances in X-ray Imaging)
Abstract
X-ray Fluorescence Computed Tomography (XFCT) is an emerging non-invasive imaging technique providing high-resolution molecular-level data. However, increased sensitivity with current benchtop X-ray sources comes at the cost of high 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 background noise reduction, but AI holds promise in addressing high-dose concerns. We present an optimized Swin-Conv-UNet (SCUNet) model for background noise reduction in X-ray fluorescence (XRF) images at low tracer concentrations. Our method’s effectiveness is evaluated against higher-dose images, while various denoising techniques exist for X-ray and computed tomography (CT) techniques, only a few address XFCT. The DL model is trained and assessed using augmented data, focusing on background noise reduction. Image quality is measured using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), comparing outcomes with 100% X-ray-dose images. Results demonstrate that the proposed algorithm yields high-quality images from low-dose inputs, with maximum PSNR of 39.05 and SSIM of 0.86. The model outperforms block-matching and 3D filtering (BM3D), block-matching and 4D filtering (BM4D), non-local means (NLM), denoising convolutional neural network (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 (DL); artificial intelligence (AI); X-ray fluorescence (XRF); XFCT; nanoparticles; cancer
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MDPI and ACS Style
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. J. Imaging 2024, 10, 127. https://doi.org/10.3390/jimaging10060127
AMA Style
Mahmoodian N, Rezapourian M, Inamdar AA, Kumar K, Fachet M, Hoeschen C. Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep-Learning-Based Denoising. Journal of Imaging. 2024; 10(6):127. https://doi.org/10.3390/jimaging10060127
Chicago/Turabian Style
Mahmoodian, Naghmeh, Mohammad Rezapourian, Asim Abdulsamad Inamdar, Kunal Kumar, Melanie Fachet, and Christoph Hoeschen. 2024. "Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep-Learning-Based Denoising" Journal of Imaging 10, no. 6: 127. https://doi.org/10.3390/jimaging10060127
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
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MDPI and ACS Style
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. J. Imaging 2024, 10, 127. https://doi.org/10.3390/jimaging10060127
AMA Style
Mahmoodian N, Rezapourian M, Inamdar AA, Kumar K, Fachet M, Hoeschen C. Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep-Learning-Based Denoising. Journal of Imaging. 2024; 10(6):127. https://doi.org/10.3390/jimaging10060127
Chicago/Turabian Style
Mahmoodian, Naghmeh, Mohammad Rezapourian, Asim Abdulsamad Inamdar, Kunal Kumar, Melanie Fachet, and Christoph Hoeschen. 2024. "Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep-Learning-Based Denoising" Journal of Imaging 10, no. 6: 127. https://doi.org/10.3390/jimaging10060127
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
J. Imaging, EISSN 2313-433X, Published by MDPI
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