Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
3.3
Journals
Diagnostics
Special Issues
Diagnostic Imaging in Multiple Sclerosis
share announcement

Diagnostic Imaging in Multiple Sclerosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 1858

Special Issue Editor

Dr. Emanuele Tinelli

E-Mail Website
Guest Editor
Unit of Neuroradiology, Department of Medical and Surgical Sciences, Magna Græcia University, 88100 Catanzaro, Italy
Interests: multiple sclerosis; radiology; neurology; neuroradiology; MRI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The correct classification and management of patients with multiple sclerosis requires the adoption of several methods for biomarker detection, and magnetic resonance imaging is among the most relevant. The evolution of neuroimaging techniques in the field of multiple sclerosis has allowed for more precise diagnosis and more accurate therapeutic monitoring. In addition to conventional magnetic resonance imaging techniques, advanced techniques are increasingly entering the clinical field, offering greater possibilities in the evaluation of patients with multiple sclerosis. To appropriately address patients with multiple sclerosis, it is necessary to deepen the knowledge on the physiopathological mechanisms, and magnetic resonance imaging can help for this objective.

Dr. Emanuele Tinelli
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. Diagnostics 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 2600 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

  • multiple sclerosis
  • MRI
  • neuroimaging
  • diagnostic imaging

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 (2 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

25 pages, 3728 KB  
Article
Handcrafted Versus Deep Feature Extraction Methods for MRI-Based Multiple Sclerosis Diagnosis
by Samah Yahia, Tahani Bouchrika and Wided Bouchelligua
Diagnostics 2026, 16(9), 1379; https://doi.org/10.3390/diagnostics16091379 - 1 May 2026
Viewed by 332
Abstract
Background: Despite significant advances in medical image analysis, automated diagnosis of Multiple Sclerosis (MS) from magnetic resonance imaging (MRI) remains challenging due to the complexity of 3D brain data and the variability of lesion appearance. Objective: In this work, we propose [...] Read more.
Background: Despite significant advances in medical image analysis, automated diagnosis of Multiple Sclerosis (MS) from magnetic resonance imaging (MRI) remains challenging due to the complexity of 3D brain data and the variability of lesion appearance. Objective: In this work, we propose an efficient and optimized feature extraction framework for automated MS diagnosis using FLAIR, T1-, and T2-weighted MRI. The approach enhances Decimal Descriptor Patterns (DDP) by integrating local gradient information, producing a 3D texture representation that is more discriminative and expressive. Methods: The study is divided into two main parts: (i) detection of MS, and (ii) assessment of disease progression in affected patients. In each part, features are extracted from the relevant MRI data and classified using multiple classical machine learning classifiers, including Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Logistic Regression. Furthermore, the performance of the proposed handcrafted feature-based approach was compared to features extracted using a deep learning-based model (vision–language model, VLM), specifically CLIP (Contrastive Language–Image Pretraining), enabling a clear comparison of their performance. To assess robustness and generalizability, two complementary validation strategies were adopted: (i) controlled experiments on the BrainWeb dataset under varying T1/T2 contrast conditions, and (ii) validation on a the real-world FLAIR MRI dataset, reflecting clinically relevant lesion visibility. Results: Gradient-DDP features achieve the best overall performance for MS progression, reaching up to 97% accuracy on T2-weighted MRI with SVM, while LDA and Logistic Regression also remain strong with accuracies around 83–96% on T2. For binary MS detection, the proposed method attains near-perfect results, with up to 99% accuracy on FLAIR (SVM/KNN) and 98% on T2-weighted images across SVM, while other classifiers also maintain high performance above 90%. Conclusions: Gradient-DDP provides strong consistency and transparency, offering an interpretable link between texture patterns and diagnostic outcomes. While VLM features perform well when lesion patterns are clearly defined (e.g., in T2), Gradient-DDP demonstrates greater robustness in more challenging modalities such as Flair, where deep representations may be less stable. Full article
(This article belongs to the Special Issue Diagnostic Imaging in Multiple Sclerosis)
►▼ Show Figures

Figure 1

13 pages, 1585 KB  
Article
Superiority of 3D-DIR over 3D-FLAIR in the Detection of Cortical Lesions and Correlation with Disability in Multiple Sclerosis: A Multicenter Study
by Irene Grazzini, Davide Del Roscio, Marco Cirinei, Benedetta Calchetti, Matteo Grammatico, Giulia Spossati, Lorenzo Malatesti, Teresa De Stefano, Andrea Cuneo, Sara Leonini, Ernesto Piane and Lorenzo Testaverde
Diagnostics 2025, 15(24), 3103; https://doi.org/10.3390/diagnostics15243103 - 6 Dec 2025
Viewed by 1177
Abstract
Background/Objectives: The aim of the study was to compare diagnostic performance of 3D-Double Inversion Recovery (DIR) and 3D-Fluid-Attenuated Inversion Recovery (FLAIR) sequences in the detection of brain lesions in Multiple Sclerosis (MS) patients, especially cortical ones, and to evaluate potential correlation between [...] Read more.
Background/Objectives: The aim of the study was to compare diagnostic performance of 3D-Double Inversion Recovery (DIR) and 3D-Fluid-Attenuated Inversion Recovery (FLAIR) sequences in the detection of brain lesions in Multiple Sclerosis (MS) patients, especially cortical ones, and to evaluate potential correlation between lesion number and clinical outcome. Methods: From April 2021 to July 2024, 278 MS patients (201 females, 77 males, mean age 47.01 ± 12.668 years) underwent brain MRI in three Italian Institutions using 1.5 T systems; 3D-FLAIR and 3D-DIR sequences were obtained with an identical anatomic position. Clinical disability was evaluated by the expanded disability state score (EDSS). Data analysis was performed using the Wilcoxon test for lesion count differences (primary endpoint), and Chi-square test and Spearman for EDSS correlation (secondary endpoint); a p < 0.05 was considered as statistically significant. Results: A significantly higher total number of lesions was displayed on DIR images (n = 6601) compared with FLAIR (n = 6484) (p < 0.001). The mean number of cortical lesions identified with DIR (1.56 ± 2.767) was significantly higher than the mean number of cortical lesions detected with FLAIR (0.52 ± 1.029) (p < 0.001). Conversely, FLAIR sequences detected a significantly higher mean number of subcortical lesions (9.34 ± 8.663) compared to DIR (8.94 ± 8.415) (p < 0.001). A significant correlation was found between EDSS and the number of juxtacortical and cortical lesions detected with DIR with a p < 0.001. Conclusions: 3D-DIR is superior to 3D-FLAIR in detecting cortical lesions, which are correlated to clinical disability, and it should be implemented for the diagnosis and prognostic evaluation in MS patients. Full article
(This article belongs to the Special Issue Diagnostic Imaging in Multiple Sclerosis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop