Combining Image Space and q-Space PDEs for Lossless Compression of Diffusion MR Images

TL;DR

This paper presents a novel lossless compression method for diffusion MRI data that surpasses traditional GZIP and JPEG codecs by exploiting both image space and q-space PDEs, achieving over 30% size reduction.

Contribution

The authors introduce a new lossless codec combining image space and q-space PDEs, with optimized finite element implementation, improving compression efficiency for diffusion MRI data.

Findings

Reduces diffusion MRI file sizes by over 30% compared to GZIP.

Outperforms lossless JPEG family codecs in compression rate.

Benefits from motion correction and volume coding order optimization.

Abstract

Diffusion MRI is a modern neuroimaging modality with a unique ability to acquire microstructural information by measuring water self-diffusion at the voxel level. However, it generates huge amounts of data, resulting from a large number of repeated 3D scans. Each volume samples a location in q-space, indicating the direction and strength of a diffusion sensitizing gradient during the measurement. This captures detailed information about the self-diffusion, and the tissue microstructure that restricts it. Lossless compression with GZIP is widely used to reduce the memory requirements. We introduce a novel lossless codec for diffusion MRI data. It reduces file sizes by more than 30% compared to GZIP, and also beats lossless codecs from the JPEG family. Our codec builds on recent work on lossless PDE-based compression of 3D medical images, but additionally exploits smoothness in q-space. We demonstrate that, compared to using only image space PDEs, q-space PDEs further improve compression rates. Moreover, implementing them with Finite Element Methods and a custom acceleration significantly reduces computational expense. Finally, we show that our codec clearly benefits from integrating subject motion correction, and slightly from optimizing the order in which the 3D volumes are coded.