Special Issue "Understanding Brain Cellular Structure and Brain Diseases Using MRI-Based Methods"

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 1039

Special Issue Editor

Preclinical MRI Lab, Champalimaud Centre for the Unknown, Lisbon, Portugal
Interests: quantitative MRI; multiparametric MRI; diffusion MRI; microstructure imaging and simulations

Special Issue Information

Dear Colleagues,

Over recent decades, a wide variety of magnetic resonance imaging (MRI) and spectroscopy (MRS) techniques and contrasts have been developed to characterize tissue architecture at the microscopic scale, which is orders of magnitude smaller than imaging voxels. Such quantitative techniques usually employ mathematical modelling to link various tissue properties to the MR measurements from contrasts such as diffusion, relaxometry, magnetization transfer, spectroscopy, etc. These approaches have been used to inform us regarding brain cellular structure and alterations caused by a wide range of disorders, such as Alzheimer’s disease, Parkinson’s disease, dementias, multiple sclerosis, brain tumors, etc.

This Special Issue of Brain Sciences aims to provide readers with a compilation of original research, perspectives, and up-to-date review articles focusing on the development and application of quantitative MRI biomarkers related to brain tissue microstructures. We are particularly looking for studies that investigate the role of such biomarkers in the detection, characterization, and follow-up of various brain disorders.

We cordially invite you to contribute to this Special Issue with submissions covering topics from basic research to clinical applications. We hope that it will bring the latest developments in neuroimaging to the scientific and medical community.

Dr. Andrada Ianuş
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. 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.


  • magnetic resonance imaging
  • brain microstructure
  • tissue modelling
  • diffusion MRI
  • magnetisation transfer
  • relaxometry
  • spectroscopy
  • brain disorders
  • neurodegenerative diseases
  • brain tumour microstructure
  • quantitative imaging biomarkers

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


15 pages, 1657 KiB  
Research Progress in Diffusion Spectrum Imaging
Brain Sci. 2023, 13(10), 1497; https://doi.org/10.3390/brainsci13101497 - 23 Oct 2023
Viewed by 785
Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation [...] Read more.
Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation within a voxel is intrinsically incapable of revealing the complex microstructures of brain tissues. Diffusion spectrum imaging (DSI) employs a pulse sequence with different b-values along multiple gradient directions to sample the diffusion information of water molecules in the entire q-space and then quantitatively estimates the diffusion profile using a probability density function with a high angular resolution. Studies have suggested that DSI can reliably observe the multidirectional fibers within each voxel and allow fiber tracking along different directions, which can improve fiber reconstruction reflecting the true but complicated brain structures that were not observed in the previous DTI studies. Moreover, with increasing angular resolution, DSI is able to reveal new neuroimaging biomarkers used for disease diagnosis and the prediction of disorder progression. However, so far, this method has not been used widely in clinical studies, due to its overly long scanning time and difficult post-processing. Within this context, the current paper aims to conduct a comprehensive review of DSI research, including the fundamental principles, methodology, and application progress of DSI tractography. By summarizing the DSI studies in recent years, we propose potential solutions towards the existing problem in the methodology and applications of DSI technology as follows: (1) using compressed sensing to undersample data and to reconstruct the diffusion signal may be an efficient and promising method for reducing scanning time; (2) the probability density function includes more information than the orientation distribution function, and it should be extended in application studies; and (3) large-sample study is encouraged to confirm the reliability and reproducibility of findings in clinical diseases. These findings may help deepen the understanding of the DSI method and promote its development in clinical applications. Full article
Show Figures

Figure 1

Back to TopTop