Difference between revisions of "Documentation/Nightly/Extensions/DTI ALPS"

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Image:LOAMRI-logo.png| LOAMRI Laboratory
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SUMMARY <ref>Manuscript in revire process.</ref>.
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Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) is a method proposed by Toshiaki Taoka et al. in 2017 to investigate the perivascular space (PVS) using diffusion tensor imaging (DTI). The PVS is a system of interstitial fluid-filled spaces surrounding blood vessels in the brain, playing a crucial role in waste clearance and neurovascular regulation.
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DTI-ALPS utilizes DTI data to quantify the directional diffusion of water molecules within the brain's PVS. By applying a novel algorithm, DTI-ALPS distinguishes the PVS from adjacent tissues, enabling the precise assessment of PVS morphology and alterations. This method offers several advantages. Firstly, it provides non-invasive imaging of the PVS, allowing for longitudinal studies and investigation of various neurological conditions. Additionally, DTI-ALPS enables the assessment of PVS integrity and changes associated with aging, vascular diseases, and neurodegenerative disorders, offering potential insights into disease mechanisms and diagnostic markers.
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Overall, DTI-ALPS represents a promising approach for studying the PVS and its implications in neurological health and disease. Its application may contribute to advancing our understanding of brain physiology and pathology, potentially leading to improved diagnosis, treatment, and management strategies for a range of neurological conditions.
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<gallery widths="500px" perrow="2">
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Image:DTI-ALPS-sc-1.png|First screen panel of DTI-ALPS module
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Image:DTI-ALPS-sc-2.png|DTI-ALPS module with index calculated
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Image:DTI-ALPS-sc-3-proj-label.png|DTI-ALPS Projection label
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Image:DTI-ALPS-sc-4-assoc-label.png|DTI-ALPS Association label
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* '''Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS)''': [[Documentation/{{documentation/version}}/Modules/DTI_ALPSIndex|DTI-ALPS Module]]
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{{documentation/{{documentation/version}}/module-section|Use Cases}}
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* Use Case 1: Obtain the DTI-ALPS index calculation from standard DTI image and propers anatomical labels (see details at <ref>Taoka 2017 paper<\ref>)
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**The DTI-ALPS index is a scalar value that represents the activity of the glymphatic system, which may be evaluated with diffusion images. Lower diffusivity along the perivascular space on DTI-APLS seems to reflect impairment of the glymphatic system.
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}
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{{documentation/{{documentation/version}}/extension-section|References}}
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* Toshiaki Taoka et al. "Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer's disease cases", (2017). Jpn J Radiol, Apr;35(4):172-178. PMID: 28197821 DOI: 10.1007/s11604-017-0617-z
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Repositories:
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*Source code: [https://github.com/LOAMRI/Slicer-DTI-ALPS GitHub repository]
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*Issue tracker:  [https://github.com/LOAMRI/Slicer-DTI-ALPS/issues open issues and enhancement requests]
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<references />

Latest revision as of 12:59, 17 April 2024

Home < Documentation < Nightly < Extensions < DTI ALPS


For the latest Slicer documentation, visit the read-the-docs.


Introduction and Acknowledgements

This work was funded by University of Campinas, Brazil. More information on the website Unicamp website.
Author: Antonio Carlos da S. Senra Filho, LOAMRI Laboratory (University of Campinas, Department of Cosmic Rays and Chronology )
Author: Andre Monteiro Paschoal, LOAMRI Laboratory (University of Campinas, Department of Cosmic Rays and Chronology )
Contact: Antonio Carlos da S. Senra Filho, email: acsenrafilho@alumni.usp.br

LOAMRI Laboratory  
University of Campinas  

Extension Description

DTIALPS-logo.png

Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) is a method proposed by Toshiaki Taoka et al. in 2017 to investigate the perivascular space (PVS) using diffusion tensor imaging (DTI). The PVS is a system of interstitial fluid-filled spaces surrounding blood vessels in the brain, playing a crucial role in waste clearance and neurovascular regulation.

DTI-ALPS utilizes DTI data to quantify the directional diffusion of water molecules within the brain's PVS. By applying a novel algorithm, DTI-ALPS distinguishes the PVS from adjacent tissues, enabling the precise assessment of PVS morphology and alterations. This method offers several advantages. Firstly, it provides non-invasive imaging of the PVS, allowing for longitudinal studies and investigation of various neurological conditions. Additionally, DTI-ALPS enables the assessment of PVS integrity and changes associated with aging, vascular diseases, and neurodegenerative disorders, offering potential insights into disease mechanisms and diagnostic markers.

Overall, DTI-ALPS represents a promising approach for studying the PVS and its implications in neurological health and disease. Its application may contribute to advancing our understanding of brain physiology and pathology, potentially leading to improved diagnosis, treatment, and management strategies for a range of neurological conditions.

  • First screen panel of DTI-ALPS module

  • DTI-ALPS module with index calculated

  • DTI-ALPS Projection label

  • DTI-ALPS Association label

Modules

  • Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS): DTI-ALPS Module


Use Cases

  • Use Case 1: Obtain the DTI-ALPS index calculation from standard DTI image and propers anatomical labels (see details at <ref>Taoka 2017 paper<\ref>)
    • The DTI-ALPS index is a scalar value that represents the activity of the glymphatic system, which may be evaluated with diffusion images. Lower diffusivity along the perivascular space on DTI-APLS seems to reflect impairment of the glymphatic system.

Similar Modules

N/A

References

  • Toshiaki Taoka et al. "Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer's disease cases", (2017). Jpn J Radiol, Apr;35(4):172-178. PMID: 28197821 DOI: 10.1007/s11604-017-0617-z

Information for Developers

Work in progress
 Section under construction.


Repositories:

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