论文标题
结合图像空间和Q空间PDE，用于扩散MR图像的无损压缩
Combining Image Space and q-Space PDEs for Lossless Compression of Diffusion MR Images
论文作者
论文摘要
扩散MRI是一种现代的神经影像模态，具有独特的能力，可以通过在体素水平上测量水自扩散来获取微观结构信息。但是，它会产生大量数据，这是由于大量重复的3D扫描所致。每个体积在Q空间中采样一个位置，表明在测量过程中扩散启发梯度的方向和强度。这捕获了有关自扩散的详细信息以及限制它的组织微观结构。 GZIP的无损压缩被广泛用于减少内存需求。我们引入了一种新型的无损编解码器，以进行扩散MRI数据。与GZIP相比，它将文件大小降低了30％以上，并且还击败了JPEG家族的无损编解码器。我们的编解码器基于最新的基于3D医学图像的基于无损PDE的压缩的工作，但还利用了Q空间的平滑度。我们证明，与仅使用图像空间PDE相比，Q-Space PDE进一步提高了压缩率。此外，使用有限元方法和自定义加速实施它们会大大降低计算费用。最后，我们表明我们的编解码器明显从整合主题运动校正中受益，并稍微优化了对3D卷的编码顺序。
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.
