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Applied Sciences
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MR-Based Neuroimaging, 2nd Edition
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MR-Based Neuroimaging, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Neuroscience and Neural Engineering".

Deadline for manuscript submissions: 30 October 2026 | Viewed by 1628

Special Issue Editors

Dr. Valeria Sacca

E-Mail Website
Guest Editor
Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
Interests: signal processing; machine learning; feature felection; EEG; fMRI; resting state fMRI; fMRI analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Fabiana Novellino

E-Mail Website
Guest Editor
Neuroimaging Unit, Institute of Bioimaging and Molecular Physiology, National Research Council (IBFM-CNR), Viale Europa, Catanzaro, Italy
Interests: neurodegenerative diseases; movement disorders; dementia; MRI; molecular imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue "MR-Based Neuroimaging".

This Special Issue will be focused on advancements in MRI techniques and quantitative MRI analysis, which are crucial in neuroimaging research. Contemporary and innovative analytical perspectives are now essential for uncovering MR-based biomarkers and understanding their role in the early stages of brain diseases.

This Special Issue explores a comprehensive range of MRI sequences, including functional and structural MRI, as well as diffusion tensor imaging. It will address both traditional methods and novel approaches, such as the application of machine learning and deep learning techniques.

Furthermore, this Special Issue is driven by the growing interest in understanding structural and functional connectivity through MR imaging, as well as the use of MR imaging to customize treatments for neurological disorders.

Additionally, this Special Issue addresses the challenges associated with integrating various MRI technologies as essential biomarkers for clinical use. It also outlines potential future directions, offering a roadmap for ongoing innovation.

Dr. Valeria Sacca
Dr. Fabiana Novellino
Guest Editors

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. Applied Sciences 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 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

  • MRI
  • functional MRI
  • structural MRI
  • DTI
  • machine learning
  • deep learning
  • brain biomarkers
  • functional connectivity

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Related Special Issue

Published Papers (3 papers)

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Research

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20 pages, 13534 KB  
Article
Improved MRSI in a Murine Glioma Model Using semiLASER: Refining the Metabolomics Data Obtained from Murine Models
by Zoona Javed, Gary V. Martinez, Marta Mulero-Acevedo, Ana Paula Candiota, Carles Arus, Miquel E. Cabañas and Silvia Lope-Piedrafita
Appl. Sci. 2026, 16(8), 3788; https://doi.org/10.3390/app16083788 - 13 Apr 2026
Viewed by 202
Abstract
Background: Magnetic resonance spectroscopic imaging (MRSI) offers valuable metabolic information for assessing brain tumor progression and therapeutic response, but its performance in rodent models is often hindered by the low signal-to-noise ratio (SNR) and spatially heterogeneous spectral quality, particularly in peripheral voxels. These [...] Read more.
Background: Magnetic resonance spectroscopic imaging (MRSI) offers valuable metabolic information for assessing brain tumor progression and therapeutic response, but its performance in rodent models is often hindered by the low signal-to-noise ratio (SNR) and spatially heterogeneous spectral quality, particularly in peripheral voxels. These issues reduce the number of usable spectra available for quantitative and classifier-based analyses. To address this, we implemented a multi-voxel MRSI-semiLASER sequence—widely recommended in clinical practice—on a 7T Bruker Biospec system running ParaVision 5.1 to improve spectral homogeneity in mouse brain tumor studies. Results: Compared with the vendor CSI-PRESS sequence, MRSI-semiLASER produced more uniform spectra across the grid and achieved up to a 1.2-fold SNR increase in murine glioma, enabling a 20% reduction in slice thickness without compromising spectral quality. Importantly, the sequence produced a substantial gain in the proportion of spectra amenable to analysis, particularly in outer grid voxels that frequently fail with CSI-PRESS. The implemented MRSI-semiLASER sequence and instructions are openly available to the community. Conclusions: MRSI-semiLASER improves spectral homogeneity, increases the proportion of usable spectra, and supports higher spatial detail. These technical improvements may enhance data yield per subject and may facilitate future applications such as more robust pattern recognition workflows and greater data efficiency in longitudinal studies, although such aspects were not evaluated here. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging, 2nd Edition)
17 pages, 3640 KB  
Article
A 3D Global-Patch Transformer for Brain Age Prediction Using T1-Weighted MRI with Gray and White Matter Maps
by Seung-Jun Lee, Myungeun Lee, Yoo Ri Kim and Hyung-Jeong Yang
Appl. Sci. 2026, 16(6), 3004; https://doi.org/10.3390/app16063004 - 20 Mar 2026
Viewed by 253
Abstract
With the increasing prevalence of neurodegenerative diseases driven by population aging, imaging-based biomarkers are needed to quantify brain aging at an early stage. Brain age, which estimates structural brain aging relative to chronological age, has emerged as a useful indicator. Prior work has [...] Read more.
With the increasing prevalence of neurodegenerative diseases driven by population aging, imaging-based biomarkers are needed to quantify brain aging at an early stage. Brain age, which estimates structural brain aging relative to chronological age, has emerged as a useful indicator. Prior work has mainly used T1-weighted MRI with deep learning models such as convolutional neural networks (CNNs) or transformers; however, many approaches insufficiently capture three-dimensional structural continuity and localized anatomical patterns, and tissue-specific aging in gray matter (GM) and white matter (WM) is often treated as auxiliary. To address these limitations, we propose a 3D Global–Patch Transformer framework for brain age prediction that directly processes volumetric data while jointly learning global brain structure and local anatomical features. Our model runs global and patch pathways in parallel and explicitly incorporates GM and WM structural maps alongside T1-weighted MRI to encode tissue-specific aging signals. Experiments on multiple public datasets, including IXI and OASIS, show that the proposed method reduces mean absolute error (MAE) by approximately 10–15% compared with CNN-based and single-input transformer baselines, with notably improved performance in older populations, highlighting the value of tissue-level structural information for brain age estimation. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging, 2nd Edition)
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Review

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21 pages, 1747 KB  
Review
The Role of Advanced MR Imaging in Gliomas
by Anastasia K. Zikou, Eleni Romeo, George A. Alexiou, Marios Lampros, Spyridon Voulgaris, Loukas Astrakas and Maria I. Argyropoulou
Appl. Sci. 2026, 16(2), 1027; https://doi.org/10.3390/app16021027 - 20 Jan 2026
Viewed by 803
Abstract
Gliomas are a significant health problem with a lot of imaging challenges. The role of imaging is no longer limited to only providing anatomic details, but with the advancement of Magnetic Resonance Imaging (MRI) techniques, it now permits the assessment of the freedom [...] Read more.
Gliomas are a significant health problem with a lot of imaging challenges. The role of imaging is no longer limited to only providing anatomic details, but with the advancement of Magnetic Resonance Imaging (MRI) techniques, it now permits the assessment of the freedom of water molecule movement, the microvascular structure, the hemodynamic characteristics, and the chemical makeup of certain metabolites of lesions. These advanced imaging techniques include diffusion-weighted imaging, diffusion tensor imaging, dynamic contrast-enhanced MRI, Magnetic Resonance (MR) perfusion, MR angiography, and magnetic resonance spectroscopy. their role in the diagnosis, classification, and post-treatment follow-up of gliomas, as well as their application in radiogenomics and glioma analysis with the aid of artificial intelligence, is presented and discussed. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging, 2nd Edition)
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