• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

对电导率张量成像与扩散张量磁共振电阻抗断层成像的验证。

Validation of conductivity tensor imaging against diffusion tensor magnetic resonance electrical impedance tomography.

机构信息

School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA.

Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, 66160, USA.

出版信息

Sci Rep. 2024 Aug 3;14(1):17995. doi: 10.1038/s41598-024-68551-z.

DOI:10.1038/s41598-024-68551-z
PMID:39097661
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11297941/
Abstract

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) and electrodeless conductivity tensor imaging (CTI) are two emerging modalities that can quantify low-frequency tissue anisotropic conductivity properties by assuming similar properties underlie ionic mobility and water diffusion. While both methods have potential applications to estimating neuro-modulation fields or formulating forward models used for electrical source imaging, a direct comparison of the two modalities has not yet been performed in-vitro or in-vivo. Therefore, the aim of this study was to test the equivalence of these two modalities. We scanned a tissue phantom and the head of human subject using DT-MREIT and CTI protocols and reconstructed conductivity tensor and effective low frequency conductivities. We found both gray and white matter conductivities recovered by each technique were equivalent within 0.05 S/m. Both DT-MREIT and CTI require multiple processing steps, and we further assess the effects of each factor on reconstructions and evaluate the extent to which different measurement mechanisms potentially cause discrepancies between the two methods. Finally, we discuss the implications for spectral models of measuring conductivity using these techniques. The study further establishes the credibility of CTI as an electrodeless non-invasive method of measuring low frequency conductivity properties.

摘要

扩散张量磁共振电阻抗断层成像(DT-MREIT)和无电极电导率张量成像(CTI)是两种新兴的模式,可以通过假设离子迁移率和水扩散具有相似的特性来量化低频组织各向异性电导率特性。虽然这两种方法都有可能应用于估计神经调节场或制定用于电源成像的正向模型,但在体内或体外尚未对这两种模式进行直接比较。因此,本研究的目的是测试这两种模式的等效性。我们使用 DT-MREIT 和 CTI 协议对组织体模和人体头部进行了扫描,并重建了电导率张量和有效低频电导率。我们发现,每种技术恢复的灰质和白质电导率在 0.05 S/m 内等效。DT-MREIT 和 CTI 都需要多个处理步骤,我们进一步评估了每个因素对重建的影响,并评估了不同测量机制在多大程度上可能导致两种方法之间的差异。最后,我们讨论了使用这些技术测量电导率的谱模型的意义。该研究进一步确立了 CTI 作为一种无电极、非侵入性测量低频电导率特性的方法的可信度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/9122a1b98f05/41598_2024_68551_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/15690baf5e71/41598_2024_68551_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/fbf667c82d85/41598_2024_68551_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/cb68dc04ecc3/41598_2024_68551_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/acca75d4c009/41598_2024_68551_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/d215b60bf602/41598_2024_68551_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/8a74345928bb/41598_2024_68551_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/069f1e0cabd8/41598_2024_68551_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/c1793bac3137/41598_2024_68551_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/f8e158d5088c/41598_2024_68551_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/22a6f000d391/41598_2024_68551_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/1c286bbb2f77/41598_2024_68551_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/7099742da1a4/41598_2024_68551_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/1f19ffb3175b/41598_2024_68551_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/6440da1485cf/41598_2024_68551_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/9122a1b98f05/41598_2024_68551_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/15690baf5e71/41598_2024_68551_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/fbf667c82d85/41598_2024_68551_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/cb68dc04ecc3/41598_2024_68551_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/acca75d4c009/41598_2024_68551_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/d215b60bf602/41598_2024_68551_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/8a74345928bb/41598_2024_68551_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/069f1e0cabd8/41598_2024_68551_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/c1793bac3137/41598_2024_68551_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/f8e158d5088c/41598_2024_68551_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/22a6f000d391/41598_2024_68551_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/1c286bbb2f77/41598_2024_68551_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/7099742da1a4/41598_2024_68551_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/1f19ffb3175b/41598_2024_68551_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/6440da1485cf/41598_2024_68551_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b627/11297941/9122a1b98f05/41598_2024_68551_Fig15_HTML.jpg

相似文献

1
Validation of conductivity tensor imaging against diffusion tensor magnetic resonance electrical impedance tomography.对电导率张量成像与扩散张量磁共振电阻抗断层成像的验证。
Sci Rep. 2024 Aug 3;14(1):17995. doi: 10.1038/s41598-024-68551-z.
2
Software Toolbox for Low-Frequency Conductivity and Current Density Imaging Using MRI.用于使用磁共振成像进行低频电导率和电流密度成像的软件工具箱。
IEEE Trans Biomed Eng. 2017 Nov;64(11):2505-2514. doi: 10.1109/TBME.2017.2732502.
3
Low-frequency conductivity tensor imaging with a single current injection using DT-MREIT.基于 DT-MREIT 的单次电流注入的低频电导率张量成像。
Phys Med Biol. 2021 Feb 20;66(5):055011. doi: 10.1088/1361-6560/abddcf.
4
Anisotropic Conductivity Tensor Imaging of In Vivo Canine Brain Using DT-MREIT.利用 DT-MREIT 对活体犬脑进行各向异性电导率张量成像。
IEEE Trans Med Imaging. 2017 Jan;36(1):124-131. doi: 10.1109/TMI.2016.2598546.
5
Electrical conductivity imaging by magnetic resonance electrical impedance tomography (MREIT).磁共振电阻抗断层成像(MREIT)的电阻抗成像
Magn Reson Med. 2003 Oct;50(4):875-8. doi: 10.1002/mrm.10588.
6
Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI.比较使用 MRI 的五种电导率张量模型和图像重建方法。
Molecules. 2021 Sep 10;26(18):5499. doi: 10.3390/molecules26185499.
7
Conductivity Tensor Imaging of In Vivo Human Brain and Experimental Validation Using Giant Vesicle Suspension.利用巨泡悬浮液对活体人脑的电导率张量成像及实验验证
IEEE Trans Med Imaging. 2019 Jul;38(7):1569-1577. doi: 10.1109/TMI.2018.2884440. Epub 2018 Dec 3.
8
Low-Frequency Conductivity Tensor Imaging of the Human Head In Vivo Using DT-MREIT: First Study.使用 DT-MREIT 对人体头部进行低频电导率张量成像的体内研究:初步研究。
IEEE Trans Med Imaging. 2018 Apr;37(4):966-976. doi: 10.1109/TMI.2017.2783348.
9
Anisotropic conductivity tensor imaging in MREIT using directional diffusion rate of water molecules.磁共振弹性成像中基于水分子定向扩散率的各向异性电导率张量成像
Phys Med Biol. 2014 Jun 21;59(12):2955-74. doi: 10.1088/0031-9155/59/12/2955. Epub 2014 May 19.
10
Magnetic Resonance Electrical Impedance Tomography.磁共振电阻抗断层成像。
Adv Exp Med Biol. 2022;1380:157-183. doi: 10.1007/978-3-031-03873-0_7.

引用本文的文献

1
Comparison of modelled diffusion-derived electrical conductivities found using magnetic resonance imaging.使用磁共振成像发现的模型扩散衍生电导率的比较。
Front Radiol. 2025 Jan 22;5:1492479. doi: 10.3389/fradi.2025.1492479. eCollection 2025.

本文引用的文献

1
High frequency conductivity decomposition by solving physically constraint underdetermined inverse problem in human brain.通过在人体大脑中的物理约束下求解不适定反问题,实现高频电导率分解。
Sci Rep. 2023 Feb 25;13(1):3273. doi: 10.1038/s41598-023-30344-1.
2
Magnetic Resonance Electrical Properties Tomography (MREPT).磁共振电阻抗断层成像(MREPT)。
Adv Exp Med Biol. 2022;1380:185-202. doi: 10.1007/978-3-031-03873-0_8.
3
Conductivity Tensor Imaging of the Human Brain Using Water Mapping Techniques.利用水成像技术对人脑进行电导率张量成像
Front Neurosci. 2021 Jul 30;15:694645. doi: 10.3389/fnins.2021.694645. eCollection 2021.
4
Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.基于磁共振的体内电磁场和电导率的单次电流给药测量——一种机器学习方法。
PLoS One. 2021 Jul 22;16(7):e0254690. doi: 10.1371/journal.pone.0254690. eCollection 2021.
5
Source localization of epileptic spikes using Multiple Sparse Priors.使用多个稀疏先验对癫痫尖峰进行源定位。
Clin Neurophysiol. 2021 Feb;132(2):586-597. doi: 10.1016/j.clinph.2020.10.030. Epub 2020 Dec 3.
6
Low-frequency dominant electrical conductivity imaging of in vivo human brain using high-frequency conductivity at Larmor-frequency and spherical mean diffusivity without external injection current.利用拉莫尔频率下的高频电导率和无外部注入电流的球平均扩散率对活体人脑进行低频主导电导率成像。
Neuroimage. 2021 Jan 15;225:117466. doi: 10.1016/j.neuroimage.2020.117466. Epub 2020 Oct 16.
7
Low frequency conductivity reconstruction based on a single current injection via MREIT.基于 MREIT 的单次电流注入的低频电导率重建。
Phys Med Biol. 2020 Nov 17;65(22):225016. doi: 10.1088/1361-6560/abbc4d.
8
Fast T mapping using multi-echo spin-echo MRI: A linear order approach.使用多回波自旋回波MRI的快速T映射:一种线性排序方法。
Magn Reson Med. 2020 Nov;84(5):2815-2830. doi: 10.1002/mrm.28309. Epub 2020 May 19.
9
Extracellular electrical conductivity property imaging by decomposition of high-frequency conductivity at Larmor-frequency using multi-b-value diffusion-weighted imaging.基于多 b 值扩散加权成像的拉莫尔频率下高频传导分解的细胞外电导率特性成像。
PLoS One. 2020 Apr 8;15(4):e0230903. doi: 10.1371/journal.pone.0230903. eCollection 2020.
10
Opening a new window on MR-based Electrical Properties Tomography with deep learning.基于深度学习的磁共振电学特性层析成像新技术。
Sci Rep. 2019 Jun 20;9(1):8895. doi: 10.1038/s41598-019-45382-x.