Voxlines: Streamline Transparency through Voxelization and View-Dependent Line Orders

TL;DR

This paper introduces a novel rendering technique for large tractography datasets that enables interactive exploration of deep brain structures by efficiently visualizing transparent streamlines using voxelization and view-dependent line ordering.

Contribution

The paper presents a new approximate order-independent transparency method that improves performance and visualization quality in large tractography datasets.

Findings

Achieves real-time rendering of transparent streamlines

Enhances visualization of deep brain structures

Outperforms existing software in speed and clarity

Abstract

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.