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Window width (WW) and window level (WL) adjustments aid in visualizinganatomies with a suitable contrast. However, the presence of background noisein MR images biases the calculation of default WW/WL values since itnecessitates a trade-off between enhancing contrast of foreground/anatomy ofinterest vs suppressing background/ outside the anatomy of interest. This paperproposes an intelligent algorithm to improve the automatic computation of WW/WLand provide better control for user defined windowing.This is achieved by firsteliminating the background pixels using a Deep Neural network and thencomputing WW/WL.

Optimal Windowing of MR Images using Deep Learning: An Enabler for Enhanced Visualization