Topology-corrected segmentation and local intensity estimates for improved partial volume classification of brain cortex in MRI
Topology-corrected segmentation and local intensity estimates for improved partial volume classification of brain cortex in MRI
复制标题
DOI：
10.1016/j.jneumeth.2010.02.020
复制
发表时间：
2010-05
影响因子：
3
通讯作者：
A. Rueda;O. Acosta;M. Couprie;P. Bourgeat;Olivier Salvado
中科院分区：
文献类型：
--
作者：
A. Rueda;O. Acosta;M. Couprie;P. Bourgeat;Olivier Salvado
在磁共振成像 (MRI) 中，脑结构量化的准确性和精确度经常受到部分体积 (PV) 效应的影响。 PV 是由于 MRI 的空间分辨率与解剖结构的大小相比有限。混合体素的准确分类和每个纯组织比例（分数含量）的正确估计可能有助于提高皮质厚度估计特别困难的区域（例如深脑沟）的精度。这项工作的贡献是双重的：一方面，我们提出了一种标记体素和计算组织分数内容的新方法，将检测脑沟的机制与拓扑保持算子相结合。另一方面，我们使用纯组织强度平均值的局部估计改进了混合体素分数含量的计算。使用模拟和真实 MR 数据评估准确性和精密度，并与其他现有方法进行比较，证明了我们方法的优点。拓扑校正带来了灰质（GM）分类和皮质厚度估计的显着改进。根据模拟数据，分数含量均方根误差减少了 6.3% (p<0.01)。真实数据的再现性误差降低了 8.8% (p<0.001)，Jaccard 相似性度量增加了 3.5%。此外，与手动引导的专家分割相比，相似性度量提高了 12.0% (p<0.001)。与使用其他方法进行部分体积分类后进行的测量相比，使用所提出的方法进行的厚度估计显示出更高的再现性。
In magnetic resonance imaging (MRI), accuracy and precision with which brain structures may be quantified are frequently affected by the partial volume (PV) effect. PV is due to the limited spatial resolution of MRI compared to the size of anatomical structures. Accurate classification of mixed voxels and correct estimation of the proportion of each pure tissue (fractional content) may help to increase the precision of cortical thickness estimation in regions where this measure is particularly difficult, such as deep sulci. The contribution of this work is twofold: on the one hand, we propose a new method to label voxels and compute tissue fractional content, integrating a mechanism for detecting sulci with topology preserving operators. On the other hand, we improve the computation of the fractional content of mixed voxels using local estimation of pure tissue intensity means. Accuracy and precision were assessed using simulated and real MR data and comparison with other existing approaches demonstrated the benefits of our method. Significant improvements in gray matter (GM) classification and cortical thickness estimation were brought by the topology correction. The fractional content root mean squared error diminished by 6.3% (p<0.01) on simulated data. The reproducibility error decreased by 8.8% (p<0.001) and the Jaccard similarity measure increased by 3.5% on real data. Furthermore, compared with manually guided expert segmentations, the similarity measure was improved by 12.0% (p<0.001). Thickness estimation with the proposed method showed a higher reproducibility compared with the measure performed after partial volume classification using other methods.