Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.2
Journals
Tomography
Special Issues
Celebrate the 10th Anniversary of Tomography
share announcement

Celebrate the 10th Anniversary of Tomography

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

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 7800

Special Issue Editor

Dr. Pascal N. Tyrrell

E-Mail Website
Guest Editor
1. Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
2. Department of Statistical Sciences, University of Toronto, Toronto, ON M5T 1W7, Canada
3. Institute of Medical Science, University of Toronto, Toronto, ON M5T 1W7, Canada
Interests: artificial Intelligence; biostatistics; machine learning; medical images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In 2025, we proudly mark the 10th Anniversary of Tomography (ISSN: 2379-139X), a journal committed to advancing the frontiers of biomedical imaging. Over the past decade, Tomography has become a leading platform for disseminating groundbreaking discoveries and fostering innovation in imaging technologies, bridging basic research, clinical applications, and cutting-edge developments in imaging data science and artificial intelligence.

To celebrate this milestone, we are curating a Special Issue that reflects on the progress and transformative impact of biomedical imaging over the past 10 years. This Special Issue will feature research that exemplifies excellence in imaging science, showcasing advancements in imaging technologies, novel contrast mechanisms, AI-driven innovations, and interdisciplinary approaches that have shaped the field.

We warmly invite researchers to contribute original research articles, comprehensive reviews, and perspective pieces. Submissions should align with the journal’s mission to advance biomedical imaging across all scales and modalities, including but not limited to the following topics:

  • Cross-sectional imaging (e.g., US, CT, MRI, PET);
  • Optical and electronic microscopy;
  • Photoacoustic and bioluminescence imaging;
  • Cryo-electron microscopy and optical computed tomography;
  • Innovations in imaging hardware, software, and informatics;
  • Novel imaging contrast agents and chemical probes;
  • Artificial intelligence applications in biomedical imaging.

This Special Issue serves as both a tribute to the extraordinary achievements of the imaging community and a forward-looking exploration of the limitless possibilities in tomography. By contributing, you join us in celebrating a decade of innovation and charting the course for future advancements in imaging science.

We look forward to receiving your submissions and collaborating to highlight the remarkable progress in biomedical imaging. Let us commemorate this anniversary by continuing to push the boundaries of knowledge and technology in healthcare and life sciences.

Dr. Pascal N. Tyrrell
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 250 words) can be sent to the Editorial Office for assessment.

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

  • cross-sectional imaging
  • microscopy
  • photoacoustic imaging
  • bioluminescence imaging
  • cryo-EM
  • optical CT
  • imaging hardware/software
  • contrast agents
  • AI in imaging
  • tomography advancements

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

14 pages, 2254 KB  
Article
Correlation Between Radiological Features of Axillary Lymph Nodes with CD4 Count and Plasma Viral Load in Patients with HIV
by Gulten Taskin, Muzaffer Elmali, Aydin Deveci and Irem Ceren Koc
Tomography 2026, 12(1), 3; https://doi.org/10.3390/tomography12010003 - 25 Dec 2025
Viewed by 719
Abstract
Objective: Axillary lymph node changes are frequently observed in patients with HIV, yet their radiological characteristics and clinical significance remain underexplored. This study aimed to evaluate the association between axillary lymph node computed tomography (CT) features and clinical markers of immune function, including [...] Read more.
Objective: Axillary lymph node changes are frequently observed in patients with HIV, yet their radiological characteristics and clinical significance remain underexplored. This study aimed to evaluate the association between axillary lymph node computed tomography (CT) features and clinical markers of immune function, including CD4 lymphocyte count and plasma viral load, in HIV-positive patients. Materials and Methods: In this retrospective study, 113 HIV-positive patients who underwent contrast-enhanced chest CT were included. Patients were stratified by CD4 count (<200, 200–500, >500 cells/μL) and plasma viral load (<100,000 or >100,000 copies/mL). Axillary lymph node parameters—including maximum and minimum diameters, cortical thickness, hilar width, and density (Hounsfield units, HU)—were measured on multiplanar reconstructed CT images. Group differences were assessed using the Kruskal–Wallis and Mann–Whitney U tests, and Spearman’s correlation was used to evaluate associations between imaging and laboratory findings. Receiver operating characteristic (ROC) curve analysis identified optimal density thresholds. Results: Lymph node diameters, cortical thickness, and hilar width did not significantly differ between CD4 groups. However, mean lymph node density was higher in patients with CD4 < 200 cells/μL (p = 0.024). A density threshold of 84.5 HU distinguished impaired from preserved immune function (sensitivity 61.1%, specificity 71.2%). Patients with viral load >100,000 copies/mL showed increased lymph node density, minimal diameter, and cortical thickness. Conclusions: Elevated axillary lymph node density correlates with immune suppression and high viral load, suggesting its potential as a non-invasive prognostic imaging biomarker in HIV infection. Full article
(This article belongs to the Special Issue Celebrate the 10th Anniversary of Tomography)
►▼ Show Figures

Figure 1

38 pages, 751 KB  
Article
Machine Learning and Feature Selection in Pediatric Appendicitis
by John Kendall, Gabriel Gaspar, Derek Berger and Jacob Levman
Tomography 2025, 11(8), 90; https://doi.org/10.3390/tomography11080090 - 13 Aug 2025
Cited by 5 | Viewed by 2633
Abstract
Background/Objectives: Accurate prediction of pediatric appendicitis diagnosis, management, and severity is critical for clinical decision-making. We aimed to evaluate the predictive performance of a wide range of machine learning models, combined with various feature selection techniques, on a pediatric appendicitis dataset. A particular [...] Read more.
Background/Objectives: Accurate prediction of pediatric appendicitis diagnosis, management, and severity is critical for clinical decision-making. We aimed to evaluate the predictive performance of a wide range of machine learning models, combined with various feature selection techniques, on a pediatric appendicitis dataset. A particular focus was placed on the role of ultrasound (US) image-descriptive features in model performance and explainability. Methods: We conducted a retrospective cohort study on a dataset of 781 pediatric patients aged 0–18 presenting to Children’s Hospital St. Hedwig in Regensburg, Germany, between January 2016 and February 2023. We developed and validated predictive models; machine learning algorithms included the random forest, logistic regression, stochastic gradient descent, and the light gradient boosting machine (LGBM). These were paired exhaustively with feature selection methods spanning filter-based (association and prediction), embedded (LGBM and linear), and a novel redundancy-aware step-up wrapper approach. We employed a machine learning benchmarking study design where AI models were trained to predict diagnosis, management, and severity outcomes, both with and without US image-descriptive features, and evaluated on held-out testing samples. Model performance was assessed using overall accuracy and area under the receiver operating characteristic curve (AUROC). A deep learner optimized for tabular data, GANDALF, was also evaluated in these applications. Results: US features significantly improved diagnostic accuracy, supporting their use in reducing model bias. However, they were not essential for maximizing accuracy in predicting management or severity. In summary, our best-performing models were, for diagnosis, the random forest with embedded LGBM feature selection (98.1% accuracy, AUROC: 0.993), for management, the random forest without feature selection (93.9% accuracy, AUROC: 0.980), and for severity, the LGBM with filter-based association feature selection (90.1% accuracy, AUROC: 0.931). Conclusions: Our results demonstrate that high-performing, interpretable machine learning models can predict key clinical outcomes in pediatric appendicitis. US image features improve diagnostic accuracy but are not critical for predicting management or severity. Full article
(This article belongs to the Special Issue Celebrate the 10th Anniversary of Tomography)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

23 pages, 7179 KB  
Review
Acute Traumatic Aortic Injury: What the Radiologist Needs to Know
by Kristina Ramirez-Garcia, Catalina Jaramillo, Emma Ferguson, Jason Au, Erika Odisio, Gustavo S. Oderich, Daniel Ocazionez, Cihan Duran and Thanila Macedo
Tomography 2026, 12(4), 57; https://doi.org/10.3390/tomography12040057 - 13 Apr 2026
Viewed by 565
Abstract
Acute traumatic aortic injury (ATAI) is a rare but life-threatening consequence of blunt trauma that requires prompt diagnosis and accurate imaging assessment. This review presents an imaging-based approach to ATAI, with emphasis on computed tomography angiography (CTA) as the first-line modality for diagnosis, [...] Read more.
Acute traumatic aortic injury (ATAI) is a rare but life-threatening consequence of blunt trauma that requires prompt diagnosis and accurate imaging assessment. This review presents an imaging-based approach to ATAI, with emphasis on computed tomography angiography (CTA) as the first-line modality for diagnosis, grading, treatment planning, and follow-up. CTA enables the detection of both direct and indirect signs while also allowing for the assessment of lesion severity, extent, and associated findings that may influence management. Familiarity with common mimics and anatomic variants improves diagnostic confidence and helps avoid false positive interpretations. Careful protocol optimization, including multiphasic acquisition, bolus timing, and postprocessing reconstructions, can further enhance image quality and diagnostic performance. Recognition of patient-related and technical CTA artifacts, along with strategies to reduce them, including the selective use of ECG-gated CTA, may further decrease diagnostic uncertainty. We also discuss the complementary roles of emerging CT technologies and magnetic resonance angiography in selected patients. Finally, we review current classification systems, imaging-guided management, post-treatment surveillance, and potential complications. Awareness of ATAI imaging findings, protocol optimization, and diagnostic pitfalls is essential for accurate interpretation and effective multidisciplinary care. Full article
(This article belongs to the Special Issue Celebrate the 10th Anniversary of Tomography)
►▼ Show Figures

Figure 1

27 pages, 3156 KB  
Review
Cancer-Associated Fibroblasts: Clinical Applications in Imaging and Therapy
by Neda Nilforoushan, Ashkan Khavaran, Maierdan Palihati, Yashvi Patel, Anna O. Giarratana, Jeeban Paul Das and Kathleen M. Capaccione
Tomography 2025, 11(12), 143; https://doi.org/10.3390/tomography11120143 - 17 Dec 2025
Cited by 3 | Viewed by 2707
Abstract
Cancer-associated fibroblasts (CAFs) are an abundant and diverse cell population within tumor microenvironments of solid tumors. Multiple subtypes of CAFs, defined by molecular and functional markers, have been described in the literature. CAFs contribute to tumor progression by remodeling the extracellular matrix, promoting [...] Read more.
Cancer-associated fibroblasts (CAFs) are an abundant and diverse cell population within tumor microenvironments of solid tumors. Multiple subtypes of CAFs, defined by molecular and functional markers, have been described in the literature. CAFs contribute to tumor progression by remodeling the extracellular matrix, promoting immune evasion, and supporting angiogenesis and metastasis. Fibroblast activation protein (FAP) is a transmembrane serine protease minimally expressed in normal adult tissues but significantly upregulated in certain subtypes of CAFs across many solid tumors. High levels of FAP have been associated with poor prognosis in various cancers. FAP has increasingly emerged as a promising target for both imaging and therapy. Multiple FAP-targeting strategies, such as small molecules, monoclonal antibodies, drug conjugates, and radiolabeled ligands, are currently being investigated in preclinical and early clinical settings. This review provides a clinically focused overview of CAFs in the tumor microenvironment, highlighting key fibroblast markers, their associations with prognosis across various tumor types, and their utility in radiologic imaging and targeted therapy. We also discuss the potential of non-FAP fibroblast targeting molecules and the clinical rationale for more selective, subtype-specific strategies. By examining fibroblast biology through a radiologist’s lens, we aim to explore the evolving role of stromal targeting in imaging and the treatment of solid tumors. Full article
(This article belongs to the Special Issue Celebrate the 10th Anniversary of Tomography)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

22 pages, 1441 KB  
Systematic Review
Quantitative Consistency of Amide Proton Transfer-Weighted MRI for Brain Tumor Differentiation: Systematic Review of Clinical Evidence
by Julius Juhyun Chung, Tianwen Ma, Phaethon Philbrook, Toby Zhou, Adam Ezra Goldman-Yassen and Phillip Zhe Sun
Tomography 2026, 12(5), 65; https://doi.org/10.3390/tomography12050065 - 6 May 2026
Viewed by 8
Abstract
Background/Objectives: Accurate grading of brain gliomas is important, and amide proton transfer-weighted (APTw) MRI shows promise for non-invasive tumor differentiation. This study aimed to perform a comprehensive review and meta-analyses to demonstrate heterogeneity in both the diagnostic accuracy and quantitative consistency of APTw [...] Read more.
Background/Objectives: Accurate grading of brain gliomas is important, and amide proton transfer-weighted (APTw) MRI shows promise for non-invasive tumor differentiation. This study aimed to perform a comprehensive review and meta-analyses to demonstrate heterogeneity in both the diagnostic accuracy and quantitative consistency of APTw MRI in distinguishing high-grade gliomas (HGGs) from low-grade gliomas (LGGs), highlight issues with reporting standards and identify sources of heterogeneity through meta-regression. Methods: A systematic literature search was conducted between 1 January 2013 and 18 January 2026, following PRISMA guidelines. Peer-reviewed articles in English reporting diagnostic accuracy/contrast values of APTw MRI and study parameters were included. Principal component analysis (PCA) was used to extract the principal components (PCs) of the chemical exchange saturation transfer (CEST) contrast mechanism. Random-effects meta-analyses and univariate meta-regression models using individual CEST parameters and three PCs were performed. Forest plots with pooled estimates were generated. Leave-one-out meta-analysis (LOOMA) and complete case analysis were performed to examine the effects of outliers and missing data, respectively. Results: A total of 31 studies were included. Meta-analyses of the AUC and mean difference demonstrated significant heterogeneity across the studies (I2 = 73.9% & 78.2%, p < 0.001). The mean difference was moderated by one SD within the mean of the readout PC (p = 0.034) and the total PC (p = 0.02). The heterogeneity for the AUC and group mean difference was not substantially reduced by moderating nor LOOMA. The results of the meta-regression using all the data were similar to those using only data with no missing parameters. Conclusions: While APTw MRI shows promise for non-invasively distinguishing glioma grades, substantial heterogeneity in the study parameters limits generalizability. To improve consistency and comparability across studies, full reports of imaging parameters and standardization of APTw protocols are essential. Full article
(This article belongs to the Special Issue Celebrate the 10th Anniversary of Tomography)
24 pages, 622 KB  
Systematic Review
Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Review
by Alzahra Altalib, Chunhui Li and Alessandro Perelli
Tomography 2026, 12(5), 64; https://doi.org/10.3390/tomography12050064 - 6 May 2026
Viewed by 18
Abstract
Background: Cone Beam Computed Tomography (CBCT) is widely used in image-guided radiotherapy because it provides on-board volumetric imaging at relatively low doses, but its clinical utility for synthetic CT (sCT) generation remains limited by noise, scatter, artifacts, and reduced Hounsfield Unit (HU) fidelity. [...] Read more.
Background: Cone Beam Computed Tomography (CBCT) is widely used in image-guided radiotherapy because it provides on-board volumetric imaging at relatively low doses, but its clinical utility for synthetic CT (sCT) generation remains limited by noise, scatter, artifacts, and reduced Hounsfield Unit (HU) fidelity. Conditional diffusion models (CDMs) have recently emerged as a promising alternative to earlier deep learning approaches because their iterative denoising process may better preserve anatomical structure and model uncertainty. Objective: This systematic review evaluates the use of conditional diffusion models for CBCT-to-CT synthesis, with particular attention to architectural strategies, reported quantitative outcomes, and potential clinical relevance. A systematic search was conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2013 and 2024. Eleven studies met the eligibility criteria and were analyzed to address three questions: (1) Which conditional diffusion strategies have been used? (2) What outcomes have been reported? and (3) What clinical implications have been discussed? Results: Across the included studies, CDMs frequently showed promising image quality performance, especially when incorporating anatomical priors, spatial-frequency guidance, hierarchical refinement, or latent representations. However, the evidence base remains small and highly heterogeneous with respect to anatomy, dimensionality, supervision strategy, and evaluation metrics, limiting the strength of direct comparative claims. The reviewed literature suggests that conditional diffusion models are a promising direction for CBCT-to-CT synthesis, but stronger dose-aware validation, standardized reporting, and broader multicenter evaluation are still needed before routine clinical deployment. This review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO), under registration number CRD42024619240. Full article
(This article belongs to the Special Issue Celebrate the 10th Anniversary of Tomography)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop