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Abstract #3578

Magnetic Resonance Elastography Analysis using Convolutional Neural Networks

Bogdan Dzyubak1, Joel P Felmlee1, and Richard L. Ehman1
1Radiology, Mayo Clinic, Rochester, MN, United States

Magnetic Resonance Elastography (MRE) accurately predicts fibrosis by measuring liver stiffness. The subjectivity in human analysis poses the biggest challenge to stiffness measurement reproducibility, and also complicates the training of a neural network to automate the task. In this work, we present a CNN-based stiffness measurement tool, giving special attention to training and validation in context of reader subjectivity. Compared to an older automated tool used by our institution in a reader-verified workflow, the CNN reduces ROI failure rate by 50%, and has an excellent agreement in measured stiffness with reader-verified target ROIs.

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