Which filter is used to remove blurring artifact during back-projection in CT imaging?

The correct choice focuses on convolution as a technique that is essential in image processing for CT imaging. Convolution is used to apply various filters, including those that help in enhancing image quality and removing artifacts. When it comes to addressing blurring artifacts that may occur during the back-projection process, convolution is particularly effective.

In CT imaging, back-projection can sometimes lead to blurriness due to the inherent nature of the reconstruction algorithms. Applying a convolution filter helps improve image clarity and sharpness by modifying the pixel values based on the neighboring pixels, essentially refining the image and reducing unintentional blur.

Other choices, like the Gaussian filter and low-pass filter, are also forms of convolution but serve different purposes in image processing. A Gaussian filter is primarily used for smoothing and noise reduction, while a low-pass filter allows low-frequency components to pass through and attenuates high-frequency components, which can lead to some loss of detail rather than specifically targeting blurring artifacts. The median filter is known for its ability to reduce salt-and-pepper noise but is not commonly used for the particular issue of blurring in the context of CT imaging back-projection.

Thus, understanding that convolution serves as a methodology for applying various filters effectively integrates knowledge about how artifacts can