Abstract #4886

Analyzing multi-exponential T2 decay data using a neural network

Hanwen Liu^1,2, Roger Tam^3,4, John K. Kramer^2,5, and Cornelia Laule^1,2,4,6

¹Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada, ²International Collaboration on Repair Discoveries, Vancouver, BC, Canada, ³Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada, ⁴Radiology, 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

The water molecules within a single voxel may exist in different microenvironments so that the T₂ relaxation is considered as a multi-exponential decay. A few quantitative imaging techniques such as myelin water imaging attempt to extract the short T₂ component as a marker specific to myelin. However, decomposition of multi-exponential T₂ decay data is an ill-posing problem. Commonly used non-negative least squares fitting method is slow, complex and unstable, even with strong regularization and B1 correction. We used synthetic data to train a single neural network for a better and faster analysis of the multi-exponential T₂ decay data.

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