Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology.
Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology.
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DOI：
10.3389/fnins.2023.1209521
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发表时间：
2023
影响因子：
4.3
通讯作者：
Canales-Rodriguez, Erick J.
中科院分区：
文献类型：
--
作者：
Barakovic, Muhamed;Pizzolato, Marco;Tax, Chantal M. W.;Rudrapatna, Umesh;Magon, Stefano;Dyrby, Tim B.;Granziera, Cristina;Thiran, Jean-Philippe;Jones, Derek K.;Canales-Rodriguez, Erick J.
轴突半径是脑疾病的潜在生物标志物，也是决定动作电位速度的重要组织微结构参数。扩散MRI（dMRI）允许非侵入性估计轴突半径，但准确估计人脑中轴突的半径是具有挑战性的。大脑中的大多数轴突的半径小于1微米，即使使用最先进的人类MRI扫描仪，这也低于dMRI信号的灵敏度极限福尔斯。因此，需要对小轴突半径敏感的新的MRI方法。在这个概念验证调查中，我们研究了基于表面的轴突松弛过程是否可以介导轴突内T2和T1时间与轴突内半径之间的关系，如使用死后组织学测量的。在具有超强扩散梯度的3 T MRI扫描仪上使用强扩散加权（即，B = 6，000 s/mm 2）和多次反转和回波时间。在不同的回波时间收集第二个简化的扩散-T2数据集，以进一步评价模型。轴突内弛豫时间通过将扩散弛豫模型拟合到取向平均的球形平均信号来估计。我们的分析表明，提出的基于表面的弛豫模型有效地解释了估计的弛豫时间和组织学轴突半径测量在各个胼胝体区域之间的关系。使用这些组织学值，我们开发了一种新的校准方法来预测胼胝体其他区域的轴突半径。值得注意的是，预测的半径和从组织学测量确定的半径非常一致。基于表面的松弛模型预测轴突半径。
Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement. Surface-based relaxation model to predict axon radius.
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影响因子：
5.7
作者：
Barakovic M;Tax CMW;Rudrapatna U;Chamberland M;Rafael-Patino J;Granziera C;Thiran JP;Daducci A;Canales-Rodríguez EJ;Jones DK
通讯作者：
Jones DK
DOI：
10.1073/pnas.2012533117
发表时间：
2020-12-29
影响因子：
11.1
作者：
Andersson M;Kjer HM;Rafael-Patino J;Pacureanu A;Pakkenberg B;Thiran JP;Ptito M;Bech M;Bjorholm Dahl A;Andersen Dahl V;Dyrby TB
通讯作者：
Dyrby TB
影响因子：
5.7
作者：
Canales-Rodriguez, Erick Jorge;Pizzolato, Marco;Thiran, Jean-Philippe
通讯作者：
Thiran, Jean-Philippe
影响因子：
1.7
作者：
Aboitiz, F;Rodriguez, E;Zaidel, E
通讯作者：
Zaidel, E
影响因子：
4.3
作者：
Barakovic M;Girard G;Schiavi S;Romascano D;Descoteaux M;Granziera C;Jones DK;Innocenti GM;Thiran JP;Daducci A
通讯作者：
Daducci A