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MR-Based Neuroimaging
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MR-Based Neuroimaging

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 March 2025 | Viewed by 1772

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
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 will be focused on advancements in MRI techniques and quantitative MRI analysis, which are central to neuroimaging research. Nowadays, contemporary and innovative analytical perspectives are 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 covers 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 within the research community 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 of 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 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. 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
  • neurological diseases

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Published Papers (2 papers)

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Research

14 pages, 2954 KiB  
Article
Towards Sustainable Magnetic Resonance Neuro Imaging: Pathways for Energy Optimization and Cost Reduction Strategies
by Zélie Alerte, Mateusz Chodorowski, Samy Ammari, Alex Rovira, Julien Ognard and Ben Salem Douraied
Appl. Sci. 2025, 15(3), 1305; https://doi.org/10.3390/app15031305 - 27 Jan 2025
Viewed by 830
Abstract
We evaluated the energy consumption of a 3T MRI using a central monitoring system, focusing on hospital energy costs during peak winter months from 2021 to 2023. We analyzed consumption during non-productive phases like end-of-day standby and assessed their impact. For active use, [...] Read more.
We evaluated the energy consumption of a 3T MRI using a central monitoring system, focusing on hospital energy costs during peak winter months from 2021 to 2023. We analyzed consumption during non-productive phases like end-of-day standby and assessed their impact. For active use, we compared standard and AI-enhanced protocols on phantoms, scheduling high-demand protocols during off-peak hours to benefit from lower energy prices. Standard protocols consumed 3.4 to 15 kWh, while optimized protocols used 2.3 to 10.6 kWh, reducing consumption by 32% on average. Savings per scan ranged from EUR 0.03 to EUR 3.7. The electrical consumption of a brain MRI protocol is equivalent to that of 3–4 knee protocols or 2–3 lumbar spine protocols. Using AI-optimized protocols and management, 41 protocols can be completed in 12 h, up from 30, reducing daily costs by EUR 2.38 to EUR 29.18. Annually, AI-optimized protocols could save 7900 to 8800 kWh per MRI unit, totaling 10,500 to 11,600 MWh across France’s MRI fleet, equivalent to the yearly consumption of about 4700 to 5300 people. Optimizing MRI resource use can expand patient access while significantly reducing the associated energy footprint. These findings support the implementation of more sustainable practices in medical imaging without compromising care quality. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging)
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11 pages, 5368 KiB  
Article
A Novel Method Combining Radial Projection with Simultaneous Multislice Imaging for Measuring Cerebrovascular Pulse Wave Velocity
by Jeong-Min Shim, Chang-Ki Kang and Young-Don Son
Appl. Sci. 2025, 15(2), 997; https://doi.org/10.3390/app15020997 - 20 Jan 2025
Viewed by 463
Abstract
Magnetic resonance imaging (MRI) using a simultaneous multislice technique can measure dynamic vascular elasticity over time. However, conventional k-space undersampling can cause signal interference, owing to vertical projection between blood vessels within the same hemisphere. Here, we proposed a radial projection method that [...] Read more.
Magnetic resonance imaging (MRI) using a simultaneous multislice technique can measure dynamic vascular elasticity over time. However, conventional k-space undersampling can cause signal interference, owing to vertical projection between blood vessels within the same hemisphere. Here, we proposed a radial projection method that can reduce signal interference between the blood vessels and aimed to verify the theoretical and practical effects of this method. A dataset from the internal and common carotid arteries (ICA and CCA) was used for both projection methods. Pulse wave velocity (PWV) was calculated using the ICA and CCA time series, and the methods were compared using the mean absolute error of PWV. The feasibility of the radial projection method in an actual MRI environment was also evaluated. PWVs of the radial projection method were statistically indistinguishable from the ground truth. And the radial projection method was less sensitive to background noise levels and showed similar results to the ground truth. This method could effectively avoid signal interference between vessels and was feasible for use in real MRI environments, maintaining high temporal resolution even with fewer sampling timepoints. Therefore, it can contribute to the early diagnosis and treatment of cerebrovascular diseases through accurate and dynamic PWV measurements. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging)
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