title: A new compression format for fiber tracking datasets.
abstract: A single diffusion MRI streamline fiber tracking dataset may contain hundreds of thousands, and often millions of streamlines and can take up to several gigabytes of memory. This amount of data is not only heavy to compute, but also difficult to visualize and hard to store on disk (especially when dealing with a collection of brains). These problems call for a fiber-specific compression format that simplifies its manipulation. As of today, no fiber compression format has yet been adopted and the need for it is now becoming an issue for future connectomics research. In this work, we propose a new compression format, .zfib, for streamline tractography datasets reconstructed from diffusion magnetic resonance imaging (dMRI). Tracts contain a large amount of redundant information and are relatively smooth. Hence, they are highly compressible. The proposed method is a processing pipeline containing a linearization, a quantization and an encoding step. Our pipeline is tested and validated under a wide range of DTI and HARDI tractography configurations (step size, streamline number, deterministic and probabilistic tracking) and compression options. Similar to JPEG, the user has one parameter to select: a worst-case maximum tolerance error in millimeter (mm). Overall, we find a compression factor of more than 96% for a maximum error of 0.1mm without any perceptual change or change of diffusion statistics (mean fractional anisotropy and mean diffusivity) along bundles. This opens new opportunities for connectomics and tractometry applications.
authors.affiliation: Computer Science Department, Faculty of Science, Université de Sherbrooke, 2500 Boulevard Université, Sherbrooke, QC J1K 2R1, Canada; Centre d'Imagerie Moléculaire de Sherbrooke (CIMS), Centre de Recherche CHUS, Canada.
authors.forename: Caroline
authors.initials: C
authors.lastname: Presseau
authors.affiliation: Computer Science Department, Faculty of Science, Université de Sherbrooke, 2500 Boulevard Université, Sherbrooke, QC J1K 2R1, Canada; Centre d'Imagerie Moléculaire de Sherbrooke (CIMS), Centre de Recherche CHUS, Canada.
authors.forename: Pierre-Marc
authors.initials: PM
authors.lastname: Jodoin
authors.affiliation: Computer Science Department, Faculty of Science, Université de Sherbrooke, 2500 Boulevard Université, Sherbrooke, QC J1K 2R1, Canada; Centre d'Imagerie Moléculaire de Sherbrooke (CIMS), Centre de Recherche CHUS, Canada.
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Houde
authors.affiliation: Computer Science Department, Faculty of Science, Université de Sherbrooke, 2500 Boulevard Université, Sherbrooke, QC J1K 2R1, Canada; Centre d'Imagerie Moléculaire de Sherbrooke (CIMS), Centre de Recherche CHUS, Canada. Electronic address: m.descoteaux@usherbrooke.ca.
authors.forename: Maxime
authors.initials: M
authors.lastname: Descoteaux
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D063132:Connectome; D044962:Data Compression; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D066127:White Matter / Q000033:anatomy & histology*
keywords: Compression; Diffusion MRI; Diffusion Tensor Imaging (DTI); High Angular Resolution Diffusion Imaging (HARDI); Tractography