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Tomography
Special Issues
Feature Reviews for Tomography 2023
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Feature Reviews for Tomography 2023

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 4028

Special Issue Editor

Prof. Dr. Emilio Quaia

E-Mail Website
Guest Editor
Department of Radiology, University of Padova, University of Padova, 35100 Padova, Italy
Interests: liver; bowel; chest
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As Editor-in-Chief of Tomography, I am pleased to announce this Special Issue, entitled “Feature Reviews for Tomography 2023”. This Special Issue aims to be a collection of high-quality review papers from our Editorial Board Members or outside leading authors, discussing new knowledge, new cutting-edge developments, and state-of-the-art imaging science research. Artificial intelligence, patient and professional dose exposure, COVID-19 imaging research, and interventional radiology are welcome topics.

Prof. Dr. Emilio Quaia
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. Tomography 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 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

  • artificial intelligence
  • dose exposure
  • COVID-19
  • MR imaging
  • CT
  • interventional radiology

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Review

16 pages, 1136 KiB  
Review
Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction
by Jesutofunmi Ayo Fajemisin, Glebys Gonzalez, Stephen A. Rosenberg, Ghanim Ullah, Gage Redler, Kujtim Latifi, Eduardo G. Moros and Issam El Naqa
Tomography 2024, 10(9), 1439-1454; https://doi.org/10.3390/tomography10090107 - 2 Sep 2024
Viewed by 250
Abstract
Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown [...] Read more.
Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown that these extracted features may be used to build machine-learning models for the prediction of treatment outcomes of cancer patients. Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. This study aims to provide an overview of MRI radiomics features used in predicting clinical treatment outcomes with machine learning techniques. The review includes examples from different disease sites. It will also discuss the impact of magnetic field strength, sample size, and other characteristics on outcome prediction performance. Full article
(This article belongs to the Special Issue Feature Reviews for Tomography 2023)
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22 pages, 16812 KiB  
Review
Musculoskeletal Pitfalls on Molecular Imaging Studies of Oncologic Patients: How to Stay Out of Trouble
by Brooke Sarna, Ty Subhawong, Efrosyni Sfakianaki, Richard Wang, Anna Christodoulou-Vega and Fabiano N. Cardoso
Tomography 2024, 10(3), 378-399; https://doi.org/10.3390/tomography10030030 - 8 Mar 2024
Viewed by 1035
Abstract
An increasing amount of molecular imaging studies are ordered each year for an oncologic population that continues to expand and increase in age. The importance of these studies in dictating further care for oncologic patients underscores the necessity of differentiating benign from malignant [...] Read more.
An increasing amount of molecular imaging studies are ordered each year for an oncologic population that continues to expand and increase in age. The importance of these studies in dictating further care for oncologic patients underscores the necessity of differentiating benign from malignant findings, particularly for a population in whom incidental findings are common. The aim of this review is to provide pictorial examples of benign musculoskeletal pathologies which may be found on molecular imaging and which may be mistaken for malignant processes. Imaging examples are provided in the form of radiographs, bone scintigraphy, computed tomography, and fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) scans. Special attention is paid to specific features that help narrow the differential diagnosis and distinguish benign from malignant processes, with the goal of avoiding unnecessary invasive procedures. Full article
(This article belongs to the Special Issue Feature Reviews for Tomography 2023)
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13 pages, 8828 KiB  
Review
Advantages of Photon-Counting Detector CT in Aortic Imaging
by Chiara Zanon, Filippo Cademartiri, Alessandro Toniolo, Costanza Bini, Alberto Clemente, Elda Chiara Colacchio, Giulio Cabrelle, Florinda Mastro, Michele Antonello, Emilio Quaia and Alessia Pepe
Tomography 2024, 10(1), 1-13; https://doi.org/10.3390/tomography10010001 - 19 Dec 2023
Cited by 5 | Viewed by 1852
Abstract
Photon-counting Computed Tomography (PCCT) is a promising imaging technique. Using detectors that count the number and energy of photons in multiple bins, PCCT offers several advantages over conventional CT, including a higher image quality, reduced contrast agent volume, radiation doses, and artifacts. Although [...] Read more.
Photon-counting Computed Tomography (PCCT) is a promising imaging technique. Using detectors that count the number and energy of photons in multiple bins, PCCT offers several advantages over conventional CT, including a higher image quality, reduced contrast agent volume, radiation doses, and artifacts. Although PCCT is well established for cardiac imaging in assessing coronary artery disease, its application in aortic imaging remains limited. This review summarizes the available literature and provides an overview of the current use of PCCT for the diagnosis of aortic imaging, focusing mainly on endoleaks detection and characterization after endovascular aneurysm repair (EVAR), contrast dose volume, and radiation exposure reduction, particularly in patients with chronic kidney disease and in those requiring follow-up CT. Full article
(This article belongs to the Special Issue Feature Reviews for Tomography 2023)
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