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Tomography
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Cutting-Edge Applications: Artificial Intelligence and Deep Learning Revolutionizing CT and...
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Cutting-Edge Applications: Artificial Intelligence and Deep Learning Revolutionizing CT and MRI

A special issue of Tomography (ISSN 2379-139X). This special issue belongs to the section "Artificial Intelligence in Medical Imaging".

Deadline for manuscript submissions: 25 March 2026 | Viewed by 5011

Special Issue Editor

Dr. Giovanni Foti

E-Mail Website
Guest Editor
Department of Radiology, IRCCS Sacro Cuore Don Calabria Hospital, Negrar di Valpolicella, 37042 Verona, Italy
Interests: MSK imaging; CT; DECT; MRI; shoulder; hip; adrenal; liver; pancreas; lung; infectious diseases; endometriosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

The increasing use of medical imaging for diagnostic purposes, coupled with advancements in rapid computing and computational algorithms, has led to the widespread adoption of deep learning (DL) and artificial intelligence (AI) algorithms. These algorithms are not only utilized for analyzing medical images but also for their processing. While recent studies have explored the potential of producing high-quality MRI reconstructed images using AI systems based on neural networks, other studies have examined the use and diagnostic accuracy of CT and MRI images across various imaging scenarios. Examples include MRI imaging in musculoskeletal, abdominal, and prostate imaging, the pediatric brain, and the female pelvis. Regarding CT, DL and AI have been proposed for the evaluation of the assessment of various issues, from identifying fractures to detailed examinations of plaques in the cardiovascular field.

For example, faster image acquisition may lead to considerable time savings while maintaining excellent quality. Moreover, this advancement often translates to improved lesion detection capabilities.

The aim of this Special Issue is to provide a comprehensive overview of DL and AI applications to CT and MRI in clinical practice.

Therefore, researchers in the field of DL and AI, working on CT or MRI applications, are encouraged to submit their findings as original articles or reviews.

Dr. Giovanni Foti
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

  • deep learning
  • computed tomography
  • magnetic resonance imaging
  • artificial intelligence
  • imaging protocol
  • detection
  • characterization

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Published Papers (2 papers)

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Research

16 pages, 11837 KiB  
Article
Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice
by Giovanni Foti, Flavio Spoto, Thomas Mignolli, Alessandro Spezia, Luigi Romano, Guglielmo Manenti, Nicolò Cardobi and Paolo Avanzi
Tomography 2025, 11(4), 48; https://doi.org/10.3390/tomography11040048 - 17 Apr 2025
Viewed by 345
Abstract
Background: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexplored in clinical practice. Purpose: The purpose of this study was [...] Read more.
Background: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexplored in clinical practice. Purpose: The purpose of this study was to evaluate the diagnostic accuracy of 2-fold and 4-fold DL-accelerated shoulder MRI protocols compared to standard protocols in clinical practice. Materials and Methods: In this prospective single-center study, 88 consecutive patients (49 males, 39 females; mean age, 51 years) underwent shoulder MRI examinations using standard, 2-fold (DL2), and 4-fold (DL4) accelerated protocols between June 2023 and January 2024. Four independent radiologists (experience range: 4–25 years) evaluated the presence of bone marrow edema (BME), rotator cuff tears, and labral lesions. The sensitivity, specificity, and interobserver agreement were calculated. Diagnostic confidence was assessed using a 4-point scale. The impact of reader experience was analyzed by stratifying the radiologists into ≤10 and >10 years of experience. Results: Both accelerated protocols demonstrated high diagnostic accuracy. For BME detection, DL2 and DL4 achieved 100% sensitivity and specificity. In rotator cuff evaluation, DL2 showed a sensitivity of 98–100% and specificity of 99–100%, while DL4 maintained a sensitivity of 95–98% and specificity of 99–100%. Labral tear detection showed perfect sensitivity (100%) with DL2 and slightly lower sensitivity (89–100%) with DL4. Interobserver agreement was excellent across the protocols (Kendall’s W = 0.92–0.98). Reader experience did not significantly impact diagnostic performance. The area under the ROC curve was 0.94 for DL2 and 0.90 for DL4 (p = 0.32). Clinical Implications: The implementation of DL-accelerated protocols, particularly DL2, could improve workflow efficiency by reducing acquisition times by 50% while maintaining diagnostic reliability. This could increase patient throughput and accessibility to MRI examinations without compromising diagnostic quality. Conclusions: DL-accelerated shoulder MRI protocols demonstrate high diagnostic accuracy, with DL2 showing performance nearly identical to that of the standard protocol. While DL4 maintains acceptable diagnostic accuracy, it shows a slight sensitivity reduction for subtle pathologies, particularly among less experienced readers. The DL2 protocol represents an optimal balance between acquisition time reduction and diagnostic confidence. Full article
(This article belongs to the Special Issue Cutting-Edge Applications: Artificial Intelligence and Deep Learning Revolutionizing CT and MRI)
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18 pages, 3821 KiB  
Article
A Hybrid CNN-Transformer Model for Predicting N Staging and Survival in Non-Small Cell Lung Cancer Patients Based on CT-Scan
by Lingfei Wang, Chenghao Zhang and Jin Li
Tomography 2024, 10(10), 1676-1693; https://doi.org/10.3390/tomography10100123 - 10 Oct 2024
Cited by 3 | Viewed by 3814
Abstract
Accurate assessment of N staging in patients with non-small cell lung cancer (NSCLC) is critical for the development of effective treatment plans, the optimization of therapeutic strategies, and the enhancement of patient survival rates. This study proposes a hybrid model based on 3D [...] Read more.
Accurate assessment of N staging in patients with non-small cell lung cancer (NSCLC) is critical for the development of effective treatment plans, the optimization of therapeutic strategies, and the enhancement of patient survival rates. This study proposes a hybrid model based on 3D convolutional neural networks (CNNs) and transformers for predicting the N-staging and survival rates of NSCLC patients within the NSCLC radiogenomics and Nsclc-radiomics datasets. The model achieved accuracies of 0.805, 0.828, and 0.819 for the training, validation, and testing sets, respectively. By leveraging the strengths of CNNs in local feature extraction and the superior performance of transformers in global information modeling, the model significantly enhances predictive accuracy and efficacy. A comparative analysis with traditional CNN and transformer architectures demonstrates that the CNN-transformer hybrid model outperforms N-staging predictions. Furthermore, this study extracts the one-year survival rate as a feature and employs the Lasso–Cox model for survival predictions at various time intervals (1, 3, 5, and 7 years), with all survival prediction p-values being less than 0.05, illustrating the time-dependent nature of survival analysis. The application of time-dependent ROC curves further validates the model’s accuracy and reliability for survival predictions. Overall, this research provides innovative methodologies and new insights for the early diagnosis and prognostic evaluation of NSCLC. Full article
(This article belongs to the Special Issue Cutting-Edge Applications: Artificial Intelligence and Deep Learning Revolutionizing CT and MRI)
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