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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351621 | PMC |
| http://dx.doi.org/10.1007/s10334-018-0709-5 | DOI Listing |
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