Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling.
Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling.
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DOI：
10.1016/j.neuroimage.2020.117044
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发表时间：
2020-10-01
期刊：
影响因子：
5.7
通讯作者：
Thielscher A
中科院分区：
文献类型：
--
作者：
Puonti O;Van Leemput K;Saturnino GB;Siebner HR;Madsen KH;Thielscher A
经颅脑刺激（TBS）已被确立为一种用于调节和映射人脑功能的方法，并作为几种脑疾病的潜在治疗工具。典型地，在电刺激（TES）中，使用具有电极的预定位置的一刀切的方法来施加刺激，或者在磁刺激（TMS）中，使用具有线圈的预定位置的一刀切的方法来施加刺激，这忽略了个体之间的解剖变异性。然而，头部中的感应电场分布在很大程度上取决于解剖特征，这意味着需要单独定制的刺激方案用于局灶性给药。这需要个体头部解剖结构的详细模型，结合电场模拟，以找到针对给定皮质目标的最佳刺激方案。考虑到不同脑部疾病和病理的解剖和功能复杂性，考虑解剖变异性以将TBS从研究工具转化为可行的治疗选择至关重要。在这篇文章中，我们提出了一种新的方法，称为CHARM，自动分割的15个不同的头部组织的磁共振（MR）扫描。新方法相比，毫不逊色，两个免费提供的软件工具上的五个组织分割任务，同时获得合理的分割精度超过所有15个组织。该方法自动适应输入扫描的变化性，因此可以直接应用于使用不同扫描仪、序列或设置采集的临床或研究扫描。我们发现，自动分割精度的增加导致在电场模拟中的相对误差较低时，从参考分割构造的解剖头部模型。然而，改进的分割以及暗示的电场模拟也受到输入MR扫描中的系统性伪影的影响。只要不考虑伪影，这可能导致局部模拟差异高达参考模拟峰值场强的30%。最后，我们示例性地展示了在现场模拟中包括所有十五种组织类别与仅使用五种组织类别的标准方法的效果，并表明对于特定的刺激配置，局部差异可以达到峰值场强的10%。
Transcranial brain stimulation (TBS) has been established as a method for modulating and mapping the function of the human brain, and as a potential treatment tool in several brain disorders. Typically, the stimulation is applied using a one-size-fits-all approach with predetermined locations for the electrodes, in electric stimulation (TES), or the coil, in magnetic stimulation (TMS), which disregards anatomical variability between individuals. However, the induced electric field distribution in the head largely depends on anatomical features implying the need for individually tailored stimulation protocols for focal dosing. This requires detailed models of the individual head anatomy, combined with electric field simulations, to find an optimal stimulation protocol for a given cortical target. Considering the anatomical and functional complexity of different brain disorders and pathologies, it is crucial to account for the anatomical variability in order to translate TBS from a research tool into a viable option for treatment. In this article we present a new method, called CHARM, for automated segmentation of fifteen different head tissues from magnetic resonance (MR) scans. The new method compares favorably to two freely available software tools on a five-tissue segmentation task, while obtaining reasonable segmentation accuracy over all fifteen tissues. The method automatically adapts to variability in the input scans and can thus be directly applied to clinical or research scans acquired with different scanners, sequences or settings. We show that an increase in automated segmentation accuracy results in a lower relative error in electric field simulations when compared to anatomical head models constructed from reference segmentations. However, also the improved segmentations and, by implication, the electric field simulations are affected by systematic artifacts in the input MR scans. As long as the artifacts are unaccounted for, this can lead to local simulation differences up to 30% of the peak field strength on reference simulations. Finally, we exemplarily demonstrate the effect of including all fifteen tissue classes in the field simulations against the standard approach of using only five tissue classes and show that for specific stimulation configurations the local differences can reach 10% of the peak field strength.
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影响因子：
5.7
作者：
Conde, Virginia;Vollmann, Henning;Ragert, Patrick
通讯作者：
Ragert, Patrick
DOI：
10.1109/embc.2012.6347236
发表时间：
2012
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
作者：
Dannhauer M;Brooks D;Tucker D;MacLeod R
通讯作者：
MacLeod R
影响因子：
4.3
作者：
Gramfort A;Luessi M;Larson E;Engemann DA;Strohmeier D;Brodbeck C;Goj R;Jas M;Brooks T;Parkkonen L;Hämäläinen M
通讯作者：
Hämäläinen M
影响因子：
8.3
作者：
Baker JM;Rorden C;Fridriksson J
通讯作者：
Fridriksson J
影响因子：
5.7
作者：
Iglesias JE;Van Leemput K;Bhatt P;Casillas C;Dutt S;Schuff N;Truran-Sacrey D;Boxer A;Fischl B;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative