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Applied Sciences
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Computed Tomography: Technological Developments, Methods and Applications
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Computed Tomography: Technological Developments, Methods and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 613

Special Issue Editors

Dr. Viktor Nikitin

E-Mail Website
Guest Editor
Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA
Interests: tomography; inverse problems; high-performance computing
Dr. Francesco De Carlo

E-Mail Website
Guest Editor
Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA
Interests: X-ray imaging; synchrotron tomography; software development for beamline controls and image processing
Dr. Rajmund Mokso

E-Mail Website
Guest Editor
Department of Physics, The Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Interests: laboratory CT; synchrotron imaging; biomedical imaging; material science

Special Issue Information

Dear Colleagues,

The Special Issue provides a comprehensive overview of the evolving landscape of computed tomography (CT), highlighting recent advancements in technology, methodological innovations and varied applications. As such, works pushing the boundaries of CT technology, emphasizing improvements in image resolution, acquisition speed and diagnostic precision will be particularly welcome. The Special Issue extends a specific invitation for contributions focusing on innovative methodologies, including advancements in image reconstruction, segmentation and informative 3D visualization strategies. Authors are encouraged to provide a balanced synthesis of theoretical insights and practical applications. Moreover, the Special Issue aims to reveal the broader range of applications for CT, encompassing traditional fields such as biomedical imaging, as well as emerging domains such as industrial inspection, material science and geology. Researchers are invited to share their findings, case studies and practical implementations, offering valuable perspectives on the versatile applications of CT. This Special Issue will serve as a crucial platform for researchers, engineers and practitioners to contribute to and stay informed about the latest developments in CT, fostering collaboration and knowledge exchange in this dynamic and rapidly advancing field.

Dr. Viktor Nikitin
Dr. Francesco De Carlo
Dr. Rajmund Mokso
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 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • computed tomography
  • imaging methods
  • reconstruction techniques
  • image segmentation
  • 3D visualization
  • diagnostic techniques
  • biomedical imaging
  • geological tomography
  • non-destructive analysis

Published Papers (1 paper)

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Research

18 pages, 9560 KiB  
Article
Reconstructing 3D De-Blurred Structures from Limited Angles of View through Turbid Media Using Deep Learning
by Ngoc An Dang Nguyen, Hoang Nhut Huynh, Trung Nghia Tran and Koichi Shimizu
Appl. Sci. 2024, 14(5), 1689; https://doi.org/10.3390/app14051689 - 20 Feb 2024
Viewed by 474
Abstract
Recent studies in transillumination imaging for developing an optical computed tomography device for small animal and human body parts have used deep learning networks to suppress the scattering effect, estimate depth information of light-absorbing structures, and reconstruct three-dimensional images of de-blurred structures. However, [...] Read more.
Recent studies in transillumination imaging for developing an optical computed tomography device for small animal and human body parts have used deep learning networks to suppress the scattering effect, estimate depth information of light-absorbing structures, and reconstruct three-dimensional images of de-blurred structures. However, they still have limitations, such as knowing the information of the structure in advance, only processing simple structures, limited effectiveness for structures with a depth of about 15 mm, and the need to use separated deep learning networks for de-blurring and estimating information. Furthermore, the current technique cannot handle multiple structures distributed at different depths next to each other in the same image. To overcome the mentioned limitations in transillumination imaging, this study proposed a pixel-by-pixel scanning technique in combination with deep learning networks (Attention Res-UNet for scattering suppression and DenseNet-169 for depth estimation) to estimate the existence of each pixel and the relative structural depth information. The efficacy of the proposed method was evaluated through experiments that involved a complex model within a tissue-equivalent phantom and a mouse, achieving a reconstruction error of 2.18% compared to the dimensions of the ground truth when using the fully convolutional network. Furthermore, we could use the depth matrix obtained from the convolutional neural network (DenseNet-169) to reconstruct the absorbing structures using a binary thresholding method, which produced a reconstruction error of 6.82%. Therefore, only one convolutional neural network (DenseNet-169) must be used for depth estimation and explicit image reconstruction. Therefore, it reduces time and computational resources. With depth information at each pixel, reconstruction of 3D image of the de-blurred structures could be performed even from a single blurred image. These results confirm the feasibility and robustness of the proposed pixel-by-pixel scanning technique to restore the internal structure of the body, including intricate networks such as blood vessels or abnormal tissues. Full article
(This article belongs to the Special Issue Computed Tomography: Technological Developments, Methods and Applications)
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