As tractography datasets continue to grow in size, there is a need for improved visualization methods that can capture structural patterns occurring in large tractography datasets. Transparency is an increasingly important aspect of finding these patterns in large datasets but is inaccessible to tractography due to performance limitations. In this paper, we propose a rendering method that achieves performant rendering of transparent streamlines, allowing for exploration of deeper brain structures interactively. The method achieves this through a novel approximate order-independent transparency method that utilizes voxelization and caching view-dependent line orders per voxel. We compare our transparency method with existing tractography visualization software in terms of performance and the ability to capture deeper structures in the dataset.

翻译：随着纤维追踪数据集规模持续增长，亟需能够捕捉大规模纤维追踪数据集中结构模式的改进可视化方法。透明度在大型数据集中识别这些模式时日益重要，但由于性能限制，纤维追踪领域一直难以实现透明度渲染。本文提出一种能够高效渲染透明流线的渲染方法，支持对深层脑结构的交互式探索。该方法通过新型近似顺序无关透明度技术实现，该技术利用体素化并为每个体素缓存视图依赖的线条顺序。我们将所提透明度方法与现有纤维追踪可视化软件在性能及捕捉数据集中深层结构能力方面进行了对比。