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Research Progress in Diffusion Spectrum Imaging

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 ori...

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Autores principales: Sun, Fenfen, Huang, Yingwen, Wang, Jingru, Hong, Wenjun, Zhao, Zhiyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10605731/
https://www.ncbi.nlm.nih.gov/pubmed/37891866
http://dx.doi.org/10.3390/brainsci13101497
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author Sun, Fenfen
Huang, Yingwen
Wang, Jingru
Hong, Wenjun
Zhao, Zhiyong
author_facet Sun, Fenfen
Huang, Yingwen
Wang, Jingru
Hong, Wenjun
Zhao, Zhiyong
author_sort Sun, Fenfen
collection PubMed
description 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.
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spelling pubmed-106057312023-10-28 Research Progress in Diffusion Spectrum Imaging Sun, Fenfen Huang, Yingwen Wang, Jingru Hong, Wenjun Zhao, Zhiyong Brain Sci Review 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. MDPI 2023-10-23 /pmc/articles/PMC10605731/ /pubmed/37891866 http://dx.doi.org/10.3390/brainsci13101497 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Sun, Fenfen
Huang, Yingwen
Wang, Jingru
Hong, Wenjun
Zhao, Zhiyong
Research Progress in Diffusion Spectrum Imaging
title Research Progress in Diffusion Spectrum Imaging
title_full Research Progress in Diffusion Spectrum Imaging
title_fullStr Research Progress in Diffusion Spectrum Imaging
title_full_unstemmed Research Progress in Diffusion Spectrum Imaging
title_short Research Progress in Diffusion Spectrum Imaging
title_sort research progress in diffusion spectrum imaging
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10605731/
https://www.ncbi.nlm.nih.gov/pubmed/37891866
http://dx.doi.org/10.3390/brainsci13101497
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