Bao Zhiguo, Zhang Tianhao, Pan Tingting, Zhang Wei, Zhao Shilun, Liu Hua, Nie Binbin
First Affiliated Hospital of Henan University, Kaifeng, China.
Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.
Front Neurosci. 2022 Jul 28;16:954237. doi: 10.3389/fnins.2022.954237. eCollection 2022.
To construct an automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging (MEMRI) of rat brain with high accuracy, which could preserve the inherent voxel intensity and Regions of interest (ROI) morphological characteristics simultaneously.
The transformation relationship from standardized space to individual space was obtained by firstly normalizing individual image to the Paxinos space and then inversely transformed. On the other hand, all the regions defined in the atlas image were separated and resaved as binary mask images. Then, transforming the mask images into individual space the inverse transformations and reslicing using the 4th B-spline interpolation algorithm. The boundary of these transformed regions was further refined by image erosion and expansion operator, and finally combined together to generate the individual parcellations. Moreover, two groups of MEMRI images were used for evaluation. We found that the individual parcellations were satisfied, and the inherent image intensity was preserved. The statistical significance of case-control comparisons was further optimized.
We have constructed a new automatic method for individual parcellation of rat brain MEMRI images, which could preserve the inherent voxel intensity and further be beneficial in case-control statistical analyses. This method could also be extended to other imaging modalities, even other experiments species. It would facilitate the accuracy and significance of ROI-based imaging analyses.
构建一种高精度的大鼠脑锰增强磁共振成像（MEMRI）个体脑区划分自动方法，该方法能够同时保留体素固有强度和感兴趣区域（ROI）形态特征。
首先将个体图像归一化到帕西诺斯空间，然后进行逆变换，从而获得从标准化空间到个体空间的变换关系。另一方面，将图谱图像中定义的所有区域分离并重新保存为二值掩膜图像。然后，使用第4阶B样条插值算法通过逆变换将掩膜图像变换到个体空间并重新切片。这些变换区域的边界通过图像腐蚀和膨胀算子进一步细化，最后合并在一起生成个体脑区划分。此外，使用两组MEMRI图像进行评估。我们发现个体脑区划分结果令人满意，并且保留了固有图像强度。病例对照比较的统计显著性得到进一步优化。
我们构建了一种新的大鼠脑MEMRI图像个体脑区划分自动方法，该方法能够保留体素固有强度，并且在病例对照统计分析中更具优势。该方法还可扩展到其他成像模态，甚至其他实验物种。它将有助于提高基于ROI的成像分析的准确性和显著性。
