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Abstract #4854

Overcoming the Rician Noise Bias of T2* Relaxometry with an Artificial Neural Network (ANN)

Ferdinand Schweser1,2, Thomas Jochmann3, and Robert Zivadinov1,2

1Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, 2Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, United States, 3Department of Computer Science and Automation, Technische Universit├Ąt Ilmenau, Ilmenau, Germany

Rician noise represents the major source of bias in parametric fitting techniques, such as the estimation of the T2* relaxation time. This bias is particularly strong when the signal-to-noise ratio is low or T2* values are short, such as in clinical cases of severe brain or liver iron overload. In this work, we trained a deep convolutional neural network to recognize Rician noise and compute unbiased relaxation parameters from multi-echo gradient echo data.

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