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Abstract #3269

Improving Parameter Mapping in MRI Relaxometry and Multi-Echo Dixon using an Automated Spectral Denoising

Felix Lugauer1,2, Dominik Nickel3, Stephan A. R. Kannengiesser3, Samuel Barnes4, Barbara Holshouser4, Jens Wetzl1, Joachim Hornegger1, and Andreas Maier1,2

1Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany, 2Research Training Group 1773 “Heterogeneous Image Systems”, Erlangen, Germany, 3MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, 4Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, United States

Magnitude-based parameter fitting is commonly used for relaxometry and multi-echo Dixon but introduces a bias for relaxation and fat fraction estimates, particularly for a low signal-to-noise ratio and high relaxation. The application of an automated, patchwise denoising to the multi-echo image series prior to parameter fitting, considerably increased the SNR; thus, reducing the bias and standard deviation in the estimates of the fit. Our findings were validated on both numerical and in-vivo experiments.

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