Valošek Jan, Bédard Sandrine, Keřkovský Miloš, Rohan Tomáš, Cohen-Adad Julien
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Canada.
Mila - Quebec AI Institute, Montreal, Canada.
Imaging Neurosci (Camb). 2024 Feb 2;2. doi: 10.1162/imag_a_00075. eCollection 2024.
Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of spinal cord pathologies, including traumatic and non-traumatic spinal cord injury and neurodegenerative diseases. However, interpreting these imaging biomarkers is difficult due to considerable intra- and inter-subject variability. Yet, there is no clear consensus on a normalization method that would help reduce this variability and more insights into the distribution of these morphometrics are needed. In this study, we computed a database of normative values for six commonly used measures of spinal cord morphometry: cross-sectional area, anteroposterior diameter, transverse diameter, compression ratio, eccentricity, and solidity. Normative values were computed from a large open-access dataset of healthy adult volunteers (N = 203) and were brought to the common space of the PAM50 spinal cord template using a newly proposed normalization method based on linear interpolation. Compared to traditional image-based registration, the proposed normalization approach does not involve image transformations and, therefore, does not introduce distortions of spinal cord anatomy. This is a crucial consideration in preserving the integrity of the spinal cord anatomy in conditions such as spinal cord injury. This new morphometric database allows researchers to normalize based on sex and age, thereby minimizing inter-subject variability associated with demographic and biological factors. The proposed methodology is open-source and accessible through the Spinal Cord Toolbox (SCT) v6.0 and higher.
从磁共振图像计算得出的脊髓形态测量指标,可作为一系列脊髓疾病的相关预后生物标志物,包括创伤性和非创伤性脊髓损伤以及神经退行性疾病。然而,由于个体间和个体内存在相当大的变异性,解读这些影像生物标志物具有一定难度。目前,对于有助于减少这种变异性的归一化方法尚无明确共识,因此需要对这些形态测量指标的分布有更多深入了解。在本研究中,我们计算了六个常用脊髓形态测量指标的标准值数据库:横截面积、前后径、横径、压缩率、偏心率和紧实度。标准值是根据一个大型的健康成年志愿者开放获取数据集(N = 203)计算得出的,并使用一种基于线性插值的新提出的归一化方法,将其映射到PAM50脊髓模板的公共空间中。与传统的基于图像的配准方法相比,所提出的归一化方法不涉及图像变换,因此不会引入脊髓解剖结构的扭曲。在脊髓损伤等情况下,这对于保持脊髓解剖结构的完整性至关重要。这个新的形态测量数据库使研究人员能够根据性别和年龄进行归一化,从而最大限度地减少与人口统计学和生物学因素相关的个体间变异性。所提出的方法是开源的,可通过脊髓工具箱(SCT)v6.0及更高版本获取。
Imaging Neurosci (Camb). 2024-2-2
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