Special Issue "X-ray Digital Radiography and Computed Tomography"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Maria Pia Morigi
E-Mail Website
Guest Editor
Department of Physics and Astronomy “Augusto Righi”, University of Bologna, Viale Carlo Berti Pichat 6/2, 40127 Bologna, Italy
Interests: X-ray imaging; radiography; X-ray computed tomography; medical imaging; non-destructive testing; cultural heritage
Dr. Fauzia Albertin
E-Mail Website
Guest Editor
Department of Physics and Astronomy “Augusto Righi”, University of Bologna, Viale Carlo Berti Pichat 6/2, 40127 Bologna, Italy
Interests: X-ray imaging; X-ray tomography; cutting-edge X-ray techniques; cultural heritage investigations; artworks; ancient manuscripts

Special Issue Information

Dear Colleagues,

X-ray imaging methods and techniques today play a key role in many research areas and applied studies of Physics and Engineering, and the potential of X-ray imaging has made radiography and computed tomography fundamental tools to gain knowledge in the most diverse fields, from medical diagnostics to the characterization of materials and industrial components, up to the evaluation of the state of conservation of a Cultural Heritage object.

The design and development of new detectors and X-ray sources and the increase of computing power have allowed us to obtain results that were once unthinkable in terms of image quality and spatial resolution, enabling the visualization of submicron details. Research in this field is highly active due to the multiple applications of X-ray imaging.

This Special Issue aims to bring expertise and competences in different fields of X-ray imaging together, covering the main branches of X-ray imaging and being suitable for a broad audience.

This Special Issue invites researchers to submit original research papers or review articles related to any discipline in which X-ray radiography and tomography are considered. The topics of interest include but are not limited to:

  • X-ray radiography;
  • X-ray computed tomography;
  • Micro-CT;
  • NDT—Nondestructive testing;
  • X-ray imaging in Cultural Heritage studies;
  • Medical Imaging;
  • Phase-contrast X-ray imaging;
  • Emerging X-ray imaging methods and instrumentations;
  • Cutting-edge technology for X-ray imaging;
  • X-ray microscopy;
  • Synchrotron radiation X-ray imaging;
  • New-generation X-ray sources;
  • Color tomography;
  • 4D tomography;
  • Dissemination projects involving X-ray imaging and new technologies (animation, virtual and augmented reality, 3D printing, etc.). 

Dr. Maria Pia Morigi
Dr. Fauzia Albertin
Guest Editors

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 papers will be 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 1600 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
  • radiography
  • X-ray computed tomography
  • micro-CT
  • synchrotron radiation X-ray imaging
  • phase-contrast X-ray imaging
  • X-ray detectors
  • X-ray sources

Published Papers (1 paper)

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Research

Article
CORONA-Net: Diagnosing COVID-19 from X-ray Images Using Re-Initialization and Classification Networks
J. Imaging 2021, 7(5), 81; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7050081 - 28 Apr 2021
Viewed by 422
Abstract
The COVID-19 pandemic has been deemed a global health pandemic. The early detection of COVID-19 is key to combating its outbreak and could help bring this pandemic to an end. One of the biggest challenges in combating COVID-19 is accurate testing for the [...] Read more.
The COVID-19 pandemic has been deemed a global health pandemic. The early detection of COVID-19 is key to combating its outbreak and could help bring this pandemic to an end. One of the biggest challenges in combating COVID-19 is accurate testing for the disease. Utilizing the power of Convolutional Neural Networks (CNNs) to detect COVID-19 from chest X-ray images can help radiologists compare and validate their results with an automated system. In this paper, we propose a carefully designed network, dubbed CORONA-Net, that can accurately detect COVID-19 from chest X-ray images. CORONA-Net is divided into two phases: (1) The reinitialization phase and (2) the classification phase. In the reinitialization phase, the network consists of encoder and decoder networks. The objective of this phase is to train and initialize the encoder and decoder networks by a distribution that comes out of medical images. In the classification phase, the decoder network is removed from CORONA-Net, and the encoder network acts as a backbone network to fine-tune the classification phase based on the learned weights from the reinitialization phase. Extensive experiments were performed on a publicly available dataset, COVIDx, and the results show that CORONA-Net significantly outperforms the current state-of-the-art networks with an overall accuracy of 95.84%. Full article
(This article belongs to the Special Issue X-ray Digital Radiography and Computed Tomography)
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