FASSt : Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography.
FASSt : Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography.
复制标题
FASSt：通过对称自动编码器进行过滤，用于球形浅表白质纤维束成像。
DOI：
10.1007/978-3-031-47292-3_12
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Shi,Yonggang
中科院分区：
文献类型：
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作者：
Li,Yuan;Nie,Xinyu;Fu,Yao;Shi,Yonggang
表面白色物质（SWM）在人脑功能中发挥着重要作用，它包含大量的皮质-皮质连接。然而，由于难以产生完整可靠的U纤维，使得SWM相关分析落后于相对成熟的深白色物质（DWM）分析。借助一些新提出的基于表面的SWM纤维束成像算法，我们开发了一种基于对称变分自编码器（VAE）的SWM滤波方法。在这项工作中，我们首先证明了球形表示的优势，并使用三角形网格和注册球面生成这些球形道。然后，我们介绍了过滤通过对称自动编码器的球形表面白色物质纤维束成像（FASSt）框架与一个新的对称权重模块执行过滤任务的潜在空间。我们评估和比较我们的方法与最先进的基于聚类的方法从人类连接组计划（HCP）的扩散MRI数据。结果表明，我们提出的方法优于这些聚类方法，在分组一致性和地形规则性方面取得了优异的性能。
Superficial white matter (SWM) plays an important role in functioning of the human brain, and it contains a large amount of cortico-cortical connections. However, the difficulties of generating complete and reliable U-fibers make SWM-related analysis lag behind relatively matured Deep white matter (DWM) analysis. With the aid of some newly proposed surface-based SWM tractography algorithms, we have developed a specialized SWM filtering method based on a symmetric variational autoencoder (VAE). In this work, we first demonstrate the advantage of the spherical representation and generate these spherical tracts using the triangular mesh and the registered spherical surface. We then introduce the Filtering via symmetric Autoencoder for Spherical Superficial White Matter tractography (FASSt) framework with a novel symmetric weights module to perform the filtering task in a latent space. We evaluate and compare our method with the state-of-the-art clustering-based method on diffusion MRI data from Human Connectome Project (HCP). The results show that our proposed method outperform these clustering methods and achieves excellent performance in groupwise consistency and topographic regularity.