Computationally intensive image reconstruction algorithms can be made accessible to the diagnostic workflow by streaming data to remote workstations in real-time. Due to bandwidth constraints, data compression is an important tool to ensure that network transmission is not a bottleneck. However, since image quality losses are unacceptable for clinical MRI, it is important to constrain any compression losses below the thermal noise.
Here we propose a framework for online data compression based on constraining SNR loss using a custom-built compression library. Greater than 5-fold data reduction was achieved by accepting a negligible SNR loss.