LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France.
LTCI, Télécom Paris and LIX, École Polytechnique, Institut Polytechnique de Paris, Palaiseau, France.
Neuroinformatics. 2020 Oct;18(4):627-640. doi: 10.1007/s12021-020-09452-0.
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.
弥散磁共振成像纤维追踪数据集可能包含数百万条 3D 轨迹线，其表示形式可能占用数十千兆字节的内存。这些轨迹线集被称为轨迹图，常用于临床操作或研究。它们的大小使得它们难以在网络上存储、可视化、处理或交换。我们提出了一种新的压缩算法，该算法充分利用了常用追踪算法获取轨迹线的方式。我们的方法基于单位向量量化方法，结合空间变换，从而实现了低压缩和解压缩时间，以及高压缩比。例如，一个 11.5GB 的轨迹图可以压缩到 1.02GB 的文件，并在 11.3 秒内解压。此外，我们的方法允许对单独的轨迹线进行压缩和解压缩，从而减少了对大型数据集进行昂贵的核外算法的需求。最后，我们为处理更大的数据集开辟了一种实时压缩和解压缩的方法，而无需大量的 RAM（即核内处理），这可以加快网络交换速度，并加快可视化或处理的加载时间。
