Abstract #4689

Myelin Water Fraction Maps with improved Fit to Noise using TGV and conventional filters

René Schranzer^1,2, Günther Grabner¹, Alexander Weber³, Kristian Bredies⁴, Gernot Reishofer⁵, and Alexander Rauscher³

¹Department of Radiologic Technology, Carinthia University of Applied Sciences, Klagenfurt, Austria, ²Department of Engineering, University of Applied Sciences Wiener Neustadt, Wiener Neustadt, Austria, ³UBC MRI Research Centre, University of British Columbia, Vancouver, BC, Canada, ⁴Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria, ⁵Department of Radiology, Medical University of Graz, Graz, Austria

Myelin Water Imaging is the technique of choice to measure myelination changes in healthy and abnormal situations in the brain. However, calculation of myelin water fraction (MWF) maps is challenging due to the low signal-to-noise ratio in the acquired data. Here, we demonstrate different filter methods, such as TGV, Gaussian and Wiener to overcome this problem. 3D GRASE images filtered with all three methods show significant enhanced fit-to-noise (FNR) values compared to unfiltered, while TGV preserves sharper edges and detailed structures. Finally, noise reduction and thus more reliable MWF maps can lead to certain advantages in the field of MS.

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