Ahmad Sahar, Wu Ye, Yap Pew-Thian
Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Med Image Comput Comput Assist Interv. 2021 Sep-Oct;12907:390-399. Epub 2021 Sep 21.
Human brain templates are a basis for comparison of brain features across individuals. They should ideally capture an atomical details at both coarse and fine scales to facilitate comparison at varying granularity. Brain template construction typically involves spatial normalization and image fusion. While significant efforts have been dedicated to improving brain templates with sophisticated spatial normalization algorithms, image fusion is typically carried out using intensity-based averaging, causing blurring of anatomical structures. Here, we present an image fusion method that exploits cortical surfaces as guidance to help preserve details in brain templates. Our method encodes cortical boundary information given by a cortical surface mesh in a signed distance function (SDF) map. We use the SDF map to help determine localized contributions of the individual images, especially at cortical boundaries, in image fusion. Experimental results demonstrate that our method significantly improves the preservation of fine gyral and sulcal details, resulting in detailed brain templates with good surface-volume agreement.
人类大脑模板是跨个体比较大脑特征的基础。理想情况下，它们应在粗粒度和细粒度尺度上捕捉解剖学细节，以便于在不同粒度下进行比较。大脑模板构建通常涉及空间归一化和图像融合。虽然已经投入了大量精力使用复杂的空间归一化算法来改进大脑模板，但图像融合通常是基于强度平均进行的，这会导致解剖结构模糊。在这里，我们提出了一种图像融合方法，该方法利用皮质表面作为指导，以帮助保留大脑模板中的细节。我们的方法在符号距离函数（SDF）图中对由皮质表面网格给出的皮质边界信息进行编码。我们使用SDF图来帮助确定各个图像在图像融合中的局部贡献，特别是在皮质边界处。实验结果表明，我们的方法显著提高了对精细脑回和脑沟细节的保留，从而得到具有良好表面-体积一致性的详细大脑模板。
