Special Issue "Advance in CT Imaging Using Deep Learning"

A special issue of Tomography (ISSN 2379-139X).

Deadline for manuscript submissions: 31 July 2022.

Special Issue Editor

Dr. Kenny H. Cha
E-Mail Website
Guest Editor
Division of Imaging, Diagnostics and Software Reliability, OSEL/CDRH/FDA, Silver Spring, MD 20993, USA
Interests: machine learning; deep learning; computer-aided diagnosis; radiomics; study design; performance assessment

Special Issue Information

Dear Colleagues,

Advances in deep learning have significantly changed the field of medical imaging analysis. Multitude of tasks, including segmentation, abnormality detection and localization, patient risk score calculation, and synthetic image generation, have benefitted from the potential of deep learning for fast and accurate results. CT imaging, in particular, is a field where deep learning has the potential to significantly impact the state of the field. Within the diagnostic utility of CT, many different tasks have been attempted across the various anatomical areas with research directly leading to clinical implementation. Novel medical devices incorporating deep learning into CT are emerging for clinical use, including devices that automatically perform organ and lesion segmentation, provide information to aid in the finding and classifying images/diseases, and perform image reconstruction and denoising. This Special Issue will focus on research papers, perspectives, and reviews informing the readers about the advances in CT imaging using deep learning. We seek manuscripts that describe methods for image analysis and image generation using CT images. Methods for deep learning-based image denoising and reconstruction that results in images that provide diagnostic information while also showing potential improvements in clinical measures, such as time savings and dose reduction, are also welcome. Innovative methods that involve CT imaging used with deep learning are encouraged, as well as manuscript submissions describing the current state of the field, and those promising new descriptions for the future of this area.

Dr. Kenny H. Cha
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 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. Tomography is an international peer-reviewed open access quarterly 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

  • CT, deep learning
  • image reconstruction
  • denoising
  • computer-aided diagnosis
  • quantitative imaging

Published Papers

This special issue is now open for submission.
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