一种用于抑制因磁共振原始数据压缩导致的图像信噪比损失的框架。
A framework for constraining image SNR loss due to MR raw data compression.
作者信息
Restivo Matthew C, Campbell-Washburn Adrienne E, Kellman Peter, Xue Hui, Ramasawmy Rajiv, Hansen Michael S
机构信息
Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Rm B1D47, 10 Center Dr, Bethesda, MD, 20814, USA.
出版信息
MAGMA. 2019 Apr;32(2):213-225. doi: 10.1007/s10334-018-0709-5. Epub 2018 Oct 25.
INTRODUCTION
Computationally intensive image reconstruction algorithms can be used online during MRI exams by streaming data to remote high-performance computers. However, data acquisition rates often exceed the bandwidth of the available network resources creating a bottleneck. Data compression is, therefore, desired to ensure fast data transmission.
METHODS
The added noise variance due to compression was determined through statistical analysis for two compression libraries (one custom and one generic) that were implemented in this framework. Limiting the compression error variance relative to the measured thermal noise allowed for image signal-to-noise ratio loss to be explicitly constrained.
RESULTS
Achievable compression ratios are dependent on image SNR, user-defined SNR loss tolerance, and acquisition type. However, a 1% reduction in SNR yields approximately four to ninefold compression ratios across MRI acquisition strategies. For free-breathing cine data reconstructed in the cloud, the streaming bandwidth was reduced from 37 to 6.1 MB/s, alleviating the network transmission bottleneck.
CONCLUSION
Our framework enabled data compression for online reconstructions and allowed SNR loss to be constrained based on a user-defined SNR tolerance. This practical tool will enable real-time data streaming and greater than fourfold faster cloud upload times.
引言
通过将数据传输到远程高性能计算机，计算密集型图像重建算法可在MRI检查期间在线使用。然而，数据采集速率常常超过可用网络资源的带宽，从而造成瓶颈。因此，需要进行数据压缩以确保快速的数据传输。
方法
通过对在此框架中实现的两个压缩库（一个自定义库和一个通用库）进行统计分析，确定了由于压缩而增加的噪声方差。相对于测量的热噪声限制压缩误差方差，可以明确限制图像信噪比损失。
结果
可实现的压缩比取决于图像信噪比、用户定义的信噪比损失容限和采集类型。然而，信噪比降低1%会使MRI采集策略的压缩比提高约4至9倍。对于在云端重建的自由呼吸电影数据，传输带宽从37MB/s降至6.1MB/s，缓解了网络传输瓶颈。
结论
我们的框架实现了用于在线重建的数据压缩，并允许根据用户定义的信噪比容限来限制信噪比损失。这个实用工具将实现实时数据流传输，并使云端上传时间加快四倍以上。
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