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

Synopsis Magnitude-based parameter ᴀ밄tting 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 ᴀ밄tting, considerably increased the SNR; thus, reducing the bias and standard deviation in the estimates of the ᴀ밄t. Our ᴀ밄ndings were validated on both numerical and in-vivo experiments. Purpose This study evaluated the impact of an automated denoising technique on magnitude-based parameter mapping of R2* and fat fraction (FF) values. Two related applications were investigated: multi-echo relaxometry for R2* and multi-echo Dixon for water, fat and R2* estimation. Magnitude-based ᴀ밄tting is often used because it is robust against phase estimation errors. For complex-based multi-echo Dixon, errors in the phase estimation can lead to a clinically relevant bias at low fat percentages . However, for higher R2* values or low SNR, the magnitude-based ᴀ밄tting introduces an increasing bias due to noise ᴀ밄oor eᴀ洅ects, making a correct delineation of high R2* values infeasible . Additionally, the SNR of a multi-echo series for quantiᴀ밄cation is rather sensitive to the choice of the signal model, echo spacing and initial TE . Yet, pathological R2* values of 1000 and higher do occur, and an accurate iron staging during therapy would be desirable. This is prohibited with magnitude-based methods unless noise eᴀ洅ects are compensated, e.g., through echo truncation, noise ᴀ밄tting or denoising. This abstract demonstrates that an automated spectrumbased noise removal on the multi-echo signal leads to a large reduction in bias and uncertainty that is associated with magnitude-based parameter mapping.