Abstract #4854

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

Ferdinand Schweser^1,2, Thomas Jochmann³, and Robert Zivadinov^1,2

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 Universität 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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