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Pushing the Boundaries of Neuroradiology: Cutting-Edge Advanced Applications and Innovations

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A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: 28 June 2024 | Viewed by 2497

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Special Issue Editors

Dr. Carlo A. Mallio

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Guest Editor

1. Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
2. Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
Interests: imaging; radiology; radiogenomics; oncology; medical and biomedical image processing; renal cell carcinoma; artificial intelligence; contrast agents and diagnostic radiology; body composition
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Dr. Gianfranco Di Gennaro

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Department of Health Sciences, School of Medicine, University of Catanzaro “Magna Græcia”, 88100 Catanzaro, Italy
Interests: epidemiology; advanced biostatistics; statistical modelling; neuroradiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neuroradiology, the dynamic field exploring the central nervus system with radiological techniques, continually evolves as groundbreaking innovations and applications emerge. This Special Issue, "Pushing the Boundaries of Neuroradiology: Cutting-Edge Advanced Applications and Innovations", explores the forefront of this discipline.

In an era marked by technological advancements, this collection of articles spotlights the transformative impact of neuroradiology on diagnostics, treatment, and research in neurological disorders.

From state-of-the-art imaging techniques to novel applications of artificial intelligence, this Special Issue delves into the vanguard of neuroradiology, providing fresh insights and new avenues for clinicians, researchers, and practitioners.

Dr. Carlo A. Mallio
Dr. Gianfranco Di Gennaro
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. Brain Sciences 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 2200 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

neuroradiology
advanced imaging techniques
innovation in neuroimaging
artificial intelligence in neuroradiology
neurological disorders diagnosis

Published Papers (3 papers)

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Research

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13 pages, 2062 KiB

Open AccessArticle

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits

by Yeong-Jae Jeon, Shin-Eui Park and Hyeon-Man Baek

Brain Sci. 2024, 14(4), 401; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14040401 - 19 Apr 2024

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Abstract

The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person’s brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual’s brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy. Full article

(This article belongs to the Special Issue Pushing the Boundaries of Neuroradiology: Cutting-Edge Advanced Applications and Innovations)

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14 pages, 3068 KiB

Open AccessArticle

Evaluating the Efficacy of Perfusion MRI and Conventional MRI in Distinguishing Recurrent Cerebral Metastasis from Brain Radiation Necrosis

by Anders Schack, Jan Saip Aunan-Diop, Frederik A. Gerhardt, Christian Bonde Pedersen, Bo Halle, Mikkel S. Kofoed, Ljubo Markovic, Martin Wirenfeldt and Frantz Rom Poulsen

Brain Sci. 2024, 14(4), 321; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14040321 - 27 Mar 2024

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Abstract

Differentiating recurrent cerebral metastasis (CM) from brain radiation necrosis (BRN) is pivotal for guiding appropriate treatment and prognostication. Despite advances in imaging techniques, however, accurately distinguishing these conditions non-invasively is still challenging. This single-center retrospective study reviewed 32 cases (28 patients) with confirmed cerebral metastases who underwent surgical excision of lesions initially diagnosed by MRI and/or MR perfusion scans from 1 January 2015 to 30 September 2020. Diagnostic accuracy was assessed by comparing imaging findings with postoperative histopathology. Conventional MRI accurately identified recurrent CM in 75% of cases. MR perfusion scans showed significantly higher mean maximum relative cerebral blood volume (max. rCBV) in metastasis cases, indicating its potential as a discriminative biomarker. No single imaging modality could definitively distinguish CM from BRN. Survival analysis revealed gender as the only significant factor affecting overall survival, with no significant survival difference observed between patients with CM and BRN after controlling for confounding factors. This study underscores the limitations of both conventional MRI and MR perfusion scans in differentiating recurrent CM from BRN. Histopathological examination remains essential for accurate diagnosis. Further research is needed to improve the reliability of non-invasive imaging and to guide the management of patients with these post-radiation events. Full article

(This article belongs to the Special Issue Pushing the Boundaries of Neuroradiology: Cutting-Edge Advanced Applications and Innovations)

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Review

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20 pages, 13210 KiB

Open AccessReview

Overview of the Current Knowledge and Conventional MRI Characteristics of Peri- and Para-Vascular Spaces

by Marco Parillo, Federica Vaccarino, Gianfranco Di Gennaro, Sumeet Kumar, Johan Van Goethem, Bruno Beomonte Zobel, Carlo Cosimo Quattrocchi, Paul M. Parizel and Carlo Augusto Mallio

Brain Sci. 2024, 14(2), 138; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14020138 - 28 Jan 2024

Viewed by 1224

Abstract

Brain spaces around (perivascular spaces) and alongside (paravascular or Virchow–Robin spaces) vessels have gained significant attention in recent years due to the advancements of in vivo imaging tools and to their crucial role in maintaining brain health, contributing to the anatomic foundation of the glymphatic system. In fact, it is widely accepted that peri- and para-vascular spaces function as waste clearance pathways for the brain for materials such as ß-amyloid by allowing exchange between cerebrospinal fluid and interstitial fluid. Visible brain spaces on magnetic resonance imaging are often a normal finding, but they have also been associated with a wide range of neurological and systemic conditions, suggesting their potential as early indicators of intracranial pressure and neurofluid imbalance. Nonetheless, several aspects of these spaces are still controversial. This article offers an overview of the current knowledge and magnetic resonance imaging characteristics of peri- and para-vascular spaces, which can help in daily clinical practice image description and interpretation. This paper is organized into different sections, including the microscopic anatomy of peri- and para-vascular spaces, their associations with pathological and physiological events, and their differential diagnosis. Full article

(This article belongs to the Special Issue Pushing the Boundaries of Neuroradiology: Cutting-Edge Advanced Applications and Innovations)

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