TRAKO: Efficient Transmission of Tractography Data for Visualization
This work addresses the challenge of handling tens-of-gigabyte tractography data for researchers and practitioners in neuroimaging, representing an incremental improvement over existing storage methods.
The authors tackled the problem of efficiently storing, transferring, and visualizing large tractography datasets by introducing TRAKO, a new data format based on glTF with integrated compression, achieving data reductions of over 28x without loss of statistical significance.
Fiber tracking produces large tractography datasets that are tens of gigabytes in size consisting of millions of streamlines. Such vast amounts of data require formats that allow for efficient storage, transfer, and visualization. We present TRAKO, a new data format based on the Graphics Layer Transmission Format (glTF) that enables immediate graphical and hardware-accelerated processing. We integrate a state-of-the-art compression technique for vertices, streamlines, and attached scalar and property data. We then compare TRAKO to existing tractography storage methods and provide a detailed evaluation on eight datasets. TRAKO can achieve data reductions of over 28x without loss of statistical significance when used to replicate analysis from previously published studies.