CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Neuroimage. 2023 Aug 15;277:120231. doi: 10.1016/j.neuroimage.2023.120231. Epub 2023 Jun 16.
Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.
从弥散加权磁共振成像估计结构连接是一项具有挑战性的任务，部分原因是存在假阳性连接和连接权重的估计错误。在先前工作的基础上，进行了 MICCAI-CDMRI 弥散模拟连接（DiSCo）挑战，以使用新的大规模数值体模评估最先进的连接方法。体模的弥散信号是通过蒙特卡罗模拟获得的。该挑战的结果表明，参加挑战的 14 个团队选择的方法可以在复杂的数值环境中提供估计和真实连接权重之间的高相关性。此外，参加团队使用的方法能够准确识别数值数据集的二进制连接。然而，所有方法都一致地估计了特定的假阳性和假阴性连接。尽管挑战数据集无法捕捉真实大脑的复杂性，但它提供了具有已知宏观结构和微观结构真实属性的独特数据，以促进连接估计方法的发展。
