Brain Magnetic Resonance Imaging

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4314

Special Issue Editor


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Guest Editor
Department of Psychiatry & Biobehavioral Sciences, Semel Institute for Neuroscience & Human Behavior, University of California, Los Angeles, CA, USA
Interests: neuropsychology; neuroimaging; neuromodulation; focused ultrasound; neurodegeneration; psychiatric illness; neuromedical illness; machine learning

Special Issue Information

Dear Colleagues,

Within the frame of the Diagnostics journal, MRI that focuses on the diagnosis and prognosis of neuromedical and psychiatric illness is an important clinical device with a growing toolset of acquisition modalities and analytic availabilities. Many of the MRI tools which were previously relegated to the research domain (e.g., volumetric statistical analysis, machine learning recovery of missing data) have begun to migrate into clinical practice. This ongoing translation from bench to bedside offers an exciting and important opportunity to further the clinical diagnostic utility of one of the most advanced neuroimaging techniques currently available. We are pleased to invite you to submit your work to our Special Issue focusing on advances of magnetic resonance imaging for the diagnosis and prognosis of issues related to the brain.

The aim of this Special Issue is to present to the reader the state of the science for data collection, missing data recovery, analysis and clinical utility of MRI as a neurodiagnostic technique. High-quality original research and reviews are invited to be submitted. The aim is to have a collection of at least 10 articles, and the Special Issue may be printed in book form if this number is reached. This research topic brings together novel developments in the hardware and software, supporting advancements in brain MRI for the diagnosis and prognosis of neuromedical and psychiatric illness. Research areas may include (but are not limited to) the following:

  • Advancements in brain MRI data acquisition and/or analysis;
  • Application of statistical analyses to clinical utility of brain MRI;
  • Application of machine learning and artificial intelligence techniques for the recovery of missing data;
  • Application of machine learning and artificial intelligence techniques for the prediction of patient classification within specific disease groups and/or prognosis and/or response to treatment;
  • Comparison of different MRI modalities and/or analyses for the diagnosis of different disease states.

We also welcome high-quality papers in relevant fields. 

I look forward to receiving your contributions.

Dr. Taylor Kuhn
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 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. 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

  • MRI
  • brain
  • neuroimaging
  • plasticity
  • diagnosis and prognosis
  • functional imaging
  • structural imaging
  • diffusion imaging
  • perfusion imaging
  • machine learning/artificial intelligence

Published Papers (3 papers)

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15 pages, 2751 KiB  
Article
Single-Voxel MR Spectroscopy of Gliomas with s-LASER at 7T
by Martin Prener, Giske Opheim, Zahra Shams, Christian Baastrup Søndergaard, Ulrich Lindberg, Henrik B. W. Larsson, Morten Ziebell, Vibeke Andrée Larsen, Mark Bitsch Vestergaard and Olaf B. Paulson
Diagnostics 2023, 13(10), 1805; https://doi.org/10.3390/diagnostics13101805 - 19 May 2023
Cited by 2 | Viewed by 1434
Abstract
Background and Purpose: Magnetic resonance spectroscopy (MRS)—a method of analysing metabolites in vivo—has been utilized in several studies of brain glioma biomarkers at lower field strengths. At ultra-high field strengths, MRS provides an improved signal-to-noise-ratio and spectral resolution, but 7T studies on patients [...] Read more.
Background and Purpose: Magnetic resonance spectroscopy (MRS)—a method of analysing metabolites in vivo—has been utilized in several studies of brain glioma biomarkers at lower field strengths. At ultra-high field strengths, MRS provides an improved signal-to-noise-ratio and spectral resolution, but 7T studies on patients with gliomas are sparse. The purpose of this exploratory study was to evaluate the potential clinical implication of the use of single-voxel MRS at 7T to assess metabolic information on lesions in a pilot cohort of patients with grade II and III gliomas. Methods: We scanned seven patients and seven healthy controls using the semi-localization by adiabatic-selective refocusing sequence on a Philips Achieva 7T system with a standard dual-transmit head coil. The metabolic ratios were calculated relative to water and total creatine. Additionally, 2-hydroxyglutarate (2-HG) MRS was carried out in four of the patients, and the 2-HG concentration was calculated relative to water. Results: When comparing the tumour data to control regions in both patients and healthy controls, we found that the choline/creatine and myo-inositol/creatine ratios were significantly increased and that the N-acetylaspartate/creatine and the neurotransmitter glutamate/creatine ratios were significantly decreased. The N-acetylaspartate/water and glutamate/water ratios were also significantly decreased. The lactate/water and lactate/creatine ratios showed increases, although not significant. The GABA/water ratio was significantly decreased, but the GABA/creatine ratio was not. MRS spectra showed the presence of 2-HG in three of the four patients studied. Three of the patients, including the MRS 2-HG-negative patient, were operated on, and all of them had the IDH mutation. Conclusion: Our findings were consistent with the existing literature on 3T and 7T MRS. Full article
(This article belongs to the Special Issue Brain Magnetic Resonance Imaging)
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10 pages, 859 KiB  
Article
Delineation of Grade II and III Gliomas Investigated by 7T MRI: An Inter-Observer Pilot Study
by Martin Prener, Giske Opheim, Helle Juhl Simonsen, Christina Malling Engelmann, Morten Ziebell, Jonathan Carlsen and Olaf B. Paulson
Diagnostics 2023, 13(8), 1365; https://doi.org/10.3390/diagnostics13081365 - 07 Apr 2023
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Abstract
Purpose: Diffuse low-grade gliomas (DLGGs) are low-malignancy brain tumors originating from the glial cells of the brain growing continuously and infiltratively along the neural axons and infiltrating the surrounding brain tissue. DLGGs usually transform into higher malignancy, causing progressive disability and premature death. [...] Read more.
Purpose: Diffuse low-grade gliomas (DLGGs) are low-malignancy brain tumors originating from the glial cells of the brain growing continuously and infiltratively along the neural axons and infiltrating the surrounding brain tissue. DLGGs usually transform into higher malignancy, causing progressive disability and premature death. MRI scans are valuable when assessing soft tissue abnormalities, but, due to the infiltrative properties of DLGGs, delineating the tumor borders is a challenging task. Therefore, the aim of this study was to explore the difference in gross tumor volume (GTV) of DLGGs delineated from 7 Tesla and 3 Tesla MRI scans. Method: Patients were recruited at the department of neurosurgery and were scanned in both a 7T and a 3T MRI scanner prior to the operation. Two observers delineated the tumors using semi-automatic delineation software. The results from each observer were blinded to the other observer’s delineation. Results: Comparing GTVs from 7T and 3T, the percentage difference varied up to 40.4% on the T2-weighted images. The percentage difference in GTV varied up to 15.3% on the fluid-attenuated inversion recovery (FLAIR) images. On the T2-weighted images, most cases varied by approximately 15%; on the FLAIR sequence, half of the cases varied by approximately 5% and the other half by approximately 15%. The overall inter-observer agreement was near perfect, with an intraclass correlation of 0.969. The intraclass correlation was better on the FLAIR sequence than on the T2 sequence. Conclusion: Overall, the GTVs delineated from 7T images were smaller. The increase in field strength improved the inter-observer agreement only on the FLAIR sequence. Full article
(This article belongs to the Special Issue Brain Magnetic Resonance Imaging)
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12 pages, 4120 KiB  
Brief Report
Functional MRI Lateralization [M1] of dlPFC and Implications for Transcranial Magnetic Stimulation (TMS) Targeting
by Jean Rama Surya, Barshen Habelhah, Jonathan Haroon, Kennedy Mahdavi, Kaya Jordan, Sergio Becerra, Victoria Venkatraman, Chloe Deveney, Alexander Bystritsky, Taylor Kuhn and Sheldon Jordan
Diagnostics 2023, 13(16), 2690; https://doi.org/10.3390/diagnostics13162690 - 16 Aug 2023
Viewed by 955
Abstract
The present study investigates a potential method of optimizing effective strategies for the functional lateralization of the dorsolateral prefrontal cortex (dlPFC) while in a scanner. Effective hemisphere lateralization of the dlPFC is crucial for lowering the functional risks connected to specific interventions (such [...] Read more.
The present study investigates a potential method of optimizing effective strategies for the functional lateralization of the dorsolateral prefrontal cortex (dlPFC) while in a scanner. Effective hemisphere lateralization of the dlPFC is crucial for lowering the functional risks connected to specific interventions (such as neurosurgery and transcranial magnetic stimulation (TMS), as well as increasing the effectiveness of a given intervention by figuring out the optimal location. This task combines elements of creative problem solving, executive decision making based on an internal rule set, and working memory. A retrospective analysis was performed on a total of 58 unique participants (34 males, 24 females, Mage = 42.93 years, SDage = 16.38). Of these participants, 47 were classified as right-handed, 7 were classified as left-handed, and 4 were classified as ambidextrous, according to the Edinburgh Handedness Inventory. The imaging data were qualitatively judged by two trained, blinded investigators (neurologist and neuropsychologist) for dominant handedness (primary motor cortex) and dominant dorsolateral prefrontal cortex (dlPFC). The results demonstrated that 21.4% of right-handed individuals showed a dominant dlPFC localized to the right hemisphere rather than the assumed left, and 16.7% of left-handers were dominant in their left hemisphere. The task completed in the scanner might be an efficient method for localizing a potential dlPFC target for the purpose of brain stimulation (e.g., TMS), though further study replications are needed to extend and validate these findings. Full article
(This article belongs to the Special Issue Brain Magnetic Resonance Imaging)
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