Keywords: Window width, window level, deep learning, intelligent windowing, back- ground suppression, MRI, U-net
TL;DR: OPTIMAL WINDOWING OF MR IMAGES USING DEEP LEARNING: AN ENABLER FOR ENHANCED VISUALIZATION
Abstract: Window width (WW) and window level (WL) adjustments aid in visualizing anatomies with a suitable contrast. However, the presence of background noise in MR images biases the calculation of default WW/WL values since it necessitates a trade-off between enhancing contrast of foreground/anatomy of interest vs suppressing background/ outside the anatomy of interest. This paper proposes an intelligent algorithm to improve the automatic computation of WW/WL and provide better control for user defined windowing.This is achieved by first eliminating the background pixels using a Deep Neural network and then computing WW/WL.
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