Abstract #3269

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

Felix Lugauer^1,2, Dominik Nickel³, Stephan A. R. Kannengiesser³, Samuel Barnes⁴, Barbara Holshouser⁴, Jens Wetzl¹, Joachim Hornegger¹, and Andreas Maier^1,2

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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