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Abstract #4129

Constrained Lossy Compression for MR Raw Data Transmission

Matthew Restivo1, Adrienne Campbell-Washburn1, Peter Kellman1, Hui Xue1, Rajiv Ramasawmy1, and Michael Hansen1

1National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States

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.

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