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J. Imaging
Special Issues
Recent Advances in X-ray Imaging
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Recent Advances in X-ray Imaging

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 3220

Special Issue Editor

Dr. Silvia Cipiccia

E-Mail Website
Guest Editor
Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
Interests: X-ray imaging; compact light sources; coherent diffraction imaging; phase contrast imaging; multimodal imaging

Special Issue Information

Dear Colleagues,

X-ray imaging is one of the most commonly used tools for non-destructive inspections from industrial to healthcare applications, performed at synchrotron facilities and small laboratories.

Most synchrotron facilities around the world have been, or are in the process of being, upgraded to diffraction limited rings, with a dramatic increase in coherent flux. Meanwhile, new types of X-ray sources are populating the landscape between large synchrotron facilities and standard x-ray laboratory sources,  from high-brilliance sources based on inverse Compton scattering to ultrafast sources driven by high-power lasers.

The upgrade and development of new sources offer new opportunities and pose new challenges. This stimulates developments in imaging techniques, detectors, and applications. As a result, X-ray imaging is quickly changing to try and satisfy, via hardware, software, or AI-empowered solutions, the quest for increasingly higher resolution and faster acquisition.

We are seeking contributions that catch the essence of this transformation by presenting the latest advances in terms of the techniques, data processing, and applications of X-ray imaging.

Dr. Silvia Cipiccia
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • X-ray imaging
  • dynamic imaging
  • coherent diffraction imaging
  • compact light sources
  • laboratory X-ray imaging
  • multimodal imaging

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Published Papers (2 papers)

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Research

17 pages, 3237 KiB  
Article
ssc-cdi: A Memory-Efficient, Multi-GPU Package for Ptychography with Extreme Data
by Yuri Rossi Tonin, Alan Zanoni Peixinho, Mauro Luiz Brandao-Junior, Paola Ferraz and Eduardo Xavier Miqueles
J. Imaging 2024, 10(11), 286; https://doi.org/10.3390/jimaging10110286 - 7 Nov 2024
Viewed by 999
Abstract
We introduce <tt>ssc-cdi</tt>, an open-source software package from the Sirius Scientific Computing family, designed for memory-efficient, single-node multi-GPU ptychography reconstruction. <tt>ssc-cdi</tt> offers a range of reconstruction engines in Python version 3.9.2 and C++/CUDA. It aims at developing local expertise and customized solutions to [...] Read more.
We introduce <tt>ssc-cdi</tt>, an open-source software package from the Sirius Scientific Computing family, designed for memory-efficient, single-node multi-GPU ptychography reconstruction. <tt>ssc-cdi</tt> offers a range of reconstruction engines in Python version 3.9.2 and C++/CUDA. It aims at developing local expertise and customized solutions to meet the specific needs of beamlines and user community of the Brazilian Synchrotron Light Laboratory (LNLS). We demonstrate ptychographic reconstruction of beamline data and present benchmarks for the package. Results show that <tt>ssc-cdi</tt> effectively handles extreme datasets typical of modern X-ray facilities without significantly compromising performance, offering a complementary approach to well-established packages of the community and serving as a robust tool for high-resolution imaging applications. Full article
(This article belongs to the Special Issue Recent Advances in X-ray Imaging)
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15 pages, 6240 KiB  
Article
Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep-Learning-Based Denoising
by Naghmeh Mahmoodian, Mohammad Rezapourian, Asim Abdulsamad Inamdar, Kunal Kumar, Melanie Fachet and Christoph Hoeschen
J. Imaging 2024, 10(6), 127; https://doi.org/10.3390/jimaging10060127 - 22 May 2024
Viewed by 1465
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Recent Advances in X-ray Imaging)
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