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Abstract #3299

Simple Universal Codes: Lossless Compression for Lower MRI Data Transmission Rates

Teresa Zhang1, Fraser Robb2, Shreyas Vasanawala3, John Pauly1, and Greig Scott4
1Stanford University, Stanford, CA, United States, 2GE Healthcare, Aurora, OH, United States, 3Radiology, Stanford University, Stanford, CA, United States, 4Electrical Engineering, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Hybrid & Novel Systems Technology, Hybrid & Novel Systems Technology, Wireless Compression

Motivation: Wireless coil arrays are a major long term goal for MRI engineering. However, data rates and spectral bandwidth are more limited than in cabled arrays.

Goal(s): We wish to assess feasible amounts by which k-space data can be compressed prior to transmission.

Approach: We use prior data sets and explore a range of lossless compression algorithms, including lossess JPEG algorithms, Fibonacci and Huffman variants. These are compared against an estimated Shannon entropy lower bound.

Results: Because MRI k-space data has very few large integers, 3x compressions are typical. Even the most basic lossless algorithms come within about 1 bit of the Shannon limit.

Impact: MRI k-space data appears compressible by 3x before data transmission. This means band-limited wireless coil arrays (WiFi or ultra-wideband) could support image 3x higher throughput, or operate with significantly slower link rates. This would be crucial for robust performance.

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Keywords