What does GMN mean in UNCLASSIFIED

GMN operates by estimating the background noise gradient using a local window. This gradient is then subtracted from the original image, effectively removing the noise while preserving the underlying signal. The result is an image with reduced noise and improved contrast.

GMN offers several advantages, including:

• Improved image quality by reducing background noise.
• Enhanced contrast and visibility of anatomical structures.
• Reduced scan time by suppressing noise without sacrificing image detail.

GMN is commonly employed in various MRI applications, such as:

• Brain imaging, where it helps visualize small structures and detect subtle abnormalities.
• Body imaging, where it enhances the visibility of organs and tissues in abdominal and chest scans.
• Diffusion-weighted imaging (DWI), where it improves the detection of diffusion abnormalities.

While GMN is effective in noise reduction, it may have some limitations:

• It can potentially introduce artifacts into the image if the noise gradient is not accurately estimated.
• GMN may slightly reduce the sharpness of the image due to the gradient subtraction process.

Yes, GMN is a widely adopted technique and is available on most modern MRI scanners. It is typically implemented as a post-processing step and can be adjusted to optimize the noise reduction and image quality.