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

## TL;DR

This paper introduces a deep learning-based method for automatically optimizing window width and level in MR images, enhancing visualization by reducing background noise influence and allowing better user control.

## Contribution

It presents a novel deep neural network approach to eliminate background pixels, improving automatic WW/WL computation for MR image visualization.

## Key findings

- Improved contrast in MR images through optimized windowing.
- Effective background noise removal using deep learning.
- Enhanced user control over image 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.