Abstract #0152

150× acceleration of myelin water imaging data analysis by a neural network

Hanwen Liu^1,2, Qing-san Xiang^1,3, Roger Tam^3,4, Alex L. MacKay^1,3, John K. Kramer^2,5, and Cornelia Laule^1,2,3,6

¹Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada, ²International Collaboration on Repair Discoveries, Vancouver, BC, Canada, ³Radiology, University of British Columbia, Vancouver, BC, Canada, ⁴Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada, ⁵Kinesiology, University of British Columbia, Vancouver, BC, Canada, ⁶Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada

In-vivo information of myelin content is desirable for studying many brain diseases and injuries which damage myelin. Myelin water imaging (MWI) is a validated and quantitative MR method to myelin. However, the data post-processing of MWI is mathematically complex and computationally demanding. The analysis typically takes several hours for a whole brain analysis, which limits its clinical applications. Our objective was to train a neural network as an alternative method for the MWI data analysis. We found this novel approach can accelerate MWI data analysis by over 150 times.

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