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QFib: Fast and Efficient Brain Tractogram Compression.

Authors
  • Mercier, C1, 2
  • Rousseau, S3
  • Gori, P4
  • Bloch, I4
  • Boubekeur, T4
  • 1 LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France. [email protected] , (France)
  • 2 LTCI, Télécom Paris and LIX, École Polytechnique, Institut Polytechnique de Paris, Palaiseau, France. [email protected] , (France)
  • 3 LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France. [email protected] , (France)
  • 4 LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France. , (France)
Type
Published Article
Journal
Neuroinformatics
Publication Date
Oct 01, 2020
Volume
18
Issue
4
Pages
627–640
Identifiers
DOI: 10.1007/s12021-020-09452-0
PMID: 32474750
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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.

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