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QFib: Fast and Efficient Brain Tractogram Compression
C Mercier1,2, S Rousseau3, P Gori4
1LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France. corentin.mercier@telecom-paris.fr.
Neuroinformatics
|June 1, 2020
Summary
We developed a novel compression algorithm for diffusion MRI tractograms, significantly reducing file sizes for easier storage and transfer. This method enables faster processing and visualization of large neuroimaging datasets.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Data Compression
Background:
- Diffusion MRI tractograms are large, posing challenges for storage, visualization, and processing.
- Current methods struggle with efficient handling of millions of 3D streamlines.
Purpose of the Study:
- To introduce a new compression algorithm specifically designed for diffusion MRI tractograms.
- To address the limitations of large tractogram file sizes in clinical and research applications.
Main Methods:
- The algorithm utilizes unit vector quantization and spatial transformation techniques.
- It leverages the inherent properties of streamlines generated by common tracking algorithms.
Main Results:
- Achieved a high compression ratio, reducing a 11.5GB tractogram to 1.02GB.
- Demonstrated fast decompression times, with an 11.3-second example.
- Enabled individual streamline compression/decompression, facilitating in-core handling of large datasets.
Conclusions:
- The proposed method offers efficient compression and decompression for tractograms.
- It significantly reduces memory requirements and speeds up data handling.
- Paves the way for on-the-fly compression/decompression, improving network exchange and processing of large neuroimaging data.
