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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 3459

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 (5 papers)

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Research

18 pages, 7130 KiB  
Article
Improving Cerebrovascular Imaging with Deep Learning: Semantic Segmentation for Time-of-Flight Magnetic Resonance Angiography Maximum Intensity Projection Image Enhancement
by Tomonari Yamada, Takaaki Yoshimura, Shota Ichikawa and Hiroyuki Sugimori
Appl. Sci. 2025, 15(6), 3034; https://doi.org/10.3390/app15063034 - 11 Mar 2025
Viewed by 183
Abstract
Magnetic Resonance Angiography (MRA) is widely used for cerebrovascular assessment, with Time-of-Flight (TOF) MRA being a common non-contrast imaging technique. However, maximum intensity projection (MIP) images generated from TOF-MRA often include non-essential vascular structures such as external carotid branches, requiring manual editing for [...] Read more.
Magnetic Resonance Angiography (MRA) is widely used for cerebrovascular assessment, with Time-of-Flight (TOF) MRA being a common non-contrast imaging technique. However, maximum intensity projection (MIP) images generated from TOF-MRA often include non-essential vascular structures such as external carotid branches, requiring manual editing for accurate visualization of intracranial arteries. This study proposes a deep learning-based semantic segmentation approach to automate the removal of these structures, enhancing MIP image clarity while reducing manual workload. Using DeepLab v3+, a convolutional neural network model optimized for segmentation accuracy, the method achieved an average Dice Similarity Coefficient (DSC) of 0.9615 and an Intersection over Union (IoU) of 0.9261 across five-fold cross-validation. The developed system processed MRA datasets at an average speed of 16.61 frames per second, demonstrating real-time feasibility. A dedicated software tool was implemented to apply the segmentation model directly to DICOM images, enabling fully automated MIP image generation. While the model effectively removed most external carotid structures, further refinement is needed to improve venous structure suppression. These results indicate that deep learning can provide an efficient and reliable approach for automated cerebrovascular image processing, with potential applications in clinical workflows and neurovascular disease diagnosis. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging)
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42 pages, 31756 KiB  
Article
Models to Identify Small Brain White Matter Hyperintensity Lesions
by Darwin Castillo, María José Rodríguez-Álvarez, René Samaniego and Vasudevan Lakshminarayanan
Appl. Sci. 2025, 15(5), 2830; https://doi.org/10.3390/app15052830 - 6 Mar 2025
Viewed by 315
Abstract
According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke and Alzheimer’s Disease and other dementias are the second and fifth leading causes of death, respectively. In this context, detecting and classifying brain [...] Read more.
According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke and Alzheimer’s Disease and other dementias are the second and fifth leading causes of death, respectively. In this context, detecting and classifying brain lesions constitute a critical area of research in medical image processing, significantly impacting clinical practice. Traditional lesion detection, segmentation, and feature extraction methods are time-consuming and observer-dependent. In this sense, research in the machine and deep learning methods applied to medical image processing constitute one of the crucial tools for automatically learning hierarchical features to get better accuracy, quick diagnosis, treatment, and prognosis of diseases. This project aims to develop and implement deep learning models for detecting and classifying small brain White Matter hyperintensities (WMH) lesions in magnetic resonance images (MRI), specifically lesions concerning ischemic and demyelination diseases. The methods applied were the UNet and Segmenting Anything model (SAM) for segmentation, while YOLOV8 and Detectron2 (based on MaskRCNN) were also applied to detect and classify the lesions. Experimental results show a Dice coefficient (DSC) of 0.94, 0.50, 0.241, and 0.88 for segmentation of WMH lesions using the UNet, SAM, YOLOv8, and Detectron2, respectively. The Detectron2 model demonstrated an accuracy of 0.94 in detecting and 0.98 in classifying lesions, including small lesions where other models often fail. The methods developed give an outline for the detection, segmentation, and classification of small and irregular morphology brain lesions and could significantly aid clinical diagnostics, providing reliable support for physicians and improving patient outcomes. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging)
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20 pages, 2360 KiB  
Article
Estimation and Validation of the “c” Factor for Overall Cerebral Functioning in the Philadelphia Neurodevelopmental Cohort
by Tyler M. Moore, Monica E. Calkins, Daniel H. Wolf, Theodore D. Satterthwaite, Ran Barzilay, J. Cobb Scott, Kosha Ruparel, Raquel E. Gur and Ruben C. Gur
Appl. Sci. 2025, 15(4), 1697; https://doi.org/10.3390/app15041697 - 7 Feb 2025
Viewed by 494
Abstract
While both psychopathology and cognitive deficits manifest in mental health disorders, the nature of their relationship remains poorly understood. Recent research suggests a potential common factor underlying both domains. Using data from the Philadelphia Neurodevelopmental Cohort (N = 9494, ages 8–21), we estimated [...] Read more.
While both psychopathology and cognitive deficits manifest in mental health disorders, the nature of their relationship remains poorly understood. Recent research suggests a potential common factor underlying both domains. Using data from the Philadelphia Neurodevelopmental Cohort (N = 9494, ages 8–21), we estimated and validated a “c” factor representing overall cerebral functioning through a structural model combining cognitive and psychopathology indicators. The model incorporated general factors of psychopathology (“p”) and cognitive ability (“g”), along with specific sub-domain factors. We evaluated the model’s criterion validity using external measures, including parent education, neighborhood socioeconomic status, global functioning, and intracranial volume, and assessed its predictive utility for longitudinal psychosis outcomes. The model demonstrated acceptable fit (CFI = 0.98, RMSEA = 0.021, SRMR = 0.030), and the “c” factor from this model showed stronger associations with parent education (r = 0.43), neighborhood SES (r = 0.47), and intracranial volume (r = 0.39) than “p” and “g” factors alone. Additionally, baseline “c” factor scores significantly predicted psychosis spectrum outcomes at follow-up (d = 0.30–0.57). These findings support the utility of a “c” factor in capturing overall cerebral function across cognitive and psychopathology domains, with potential implications for understanding brain function, improving clinical assessment, and optimally focusing interventions. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging)
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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 1221
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 602
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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