Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep Learning-Based Denoising Article Swipe
Naghmeh Mahmoodian
,
Mohammad Rezapourian
,
Asim Abdulsamad Inamdar
,
Kunal Kumar
,
Melanie Fachet
,
Christoph Hoeschen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202403.0862.v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202403.0862.v1
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.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202403.0862.v1
- https://www.preprints.org/manuscript/202403.0862/v1/download
- OA Status
- green
- Cited By
- 1
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392925343
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- OpenAlex ID
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https://openalex.org/W4392925343Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202403.0862.v1Digital Object Identifier
- Title
-
Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep Learning-Based DenoisingWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-14Full publication date if available
- Authors
-
Naghmeh Mahmoodian, Mohammad Rezapourian, Asim Abdulsamad Inamdar, Kunal Kumar, Melanie Fachet, Christoph HoeschenList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202403.0862.v1Publisher landing page
- PDF URL
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https://www.preprints.org/manuscript/202403.0862/v1/downloadDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.preprints.org/manuscript/202403.0862/v1/downloadDirect OA link when available
- Concepts
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Computed tomography, In vivo, Tomography, Noise reduction, Preclinical imaging, Artificial intelligence, Nuclear medicine, Medical physics, Materials science, Radiology, Biomedical engineering, Computer science, Physics, Medicine, Biology, BiotechnologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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