Diffusion MRI fiber tracking datasets can contain millions of 3D streamlines, and their representation can weight tens of gigabytes of memory. These sets of streamlines are called tractograms and are often used for clinical operations or research. Their size makes them difficult to store, visualize, process or exchange over the network. We propose a new compression algorithm well-suited for tractograms, by taking advantage of the way streamlines are obtained with usual tracking algorithms. Our approach is based on unit vector quantization methods combined with a spatial transformation which results in low compression and decompression times, as well as a high compression ratio. For instance, a 11.5GB tractogram can be compressed to a 1.02GB file and decompressed in 11.3 seconds. Moreover, our method allows for the compression and decompression of individual streamlines, reducing the need for a costly out-of-core algorithm with heavy datasets. Last, we open a way toward on-the-fly compression and decompression for handling larger datasets without needing a load of RAM (i.e. in-core handling), faster network exchanges and faster loading times for visualization or processing.

扩散MRI光纤跟踪数据集可以包含数百万条3D流线，其表示形式可能会占用数十GB的内存。这些流线的集合称为束线图，通常用于临床手术或研究。它们的大小使它们难以通过网络进行存储，可视化，处理或交换。通过利用通常的跟踪算法获得流线的方式，我们提出了一种非常适合于人体图的新压缩算法。我们的方法基于单位矢量量化方法，并结合了空间变换，这导致较低的压缩和解压缩时间以及较高的压缩率。例如，可以将11.5GB的谱图压缩为1.02GB的文件，并在11.3秒内解压缩。此外，我们的方法允许对单个流线进行压缩和解压缩，从而减少了对具有大量数据集的昂贵的核外算法的需求。最后，我们提供了一种动态压缩和解压缩方式，可以处理较大的数据集，而无需加载RAM（即内核处理），更快的网络交换以及更快的可视化或处理加载时间。