What mathematical process is used to remove blurring from reconstructed CT images?

The mathematical process used to remove blurring from reconstructed CT images is convolution. In image processing, convolution is a technique where a filter (also known as a kernel) is applied to the image data. This filter contains values that help enhance or suppress specific features in the image based on the surrounding pixel values.

When applied to CT images, convolution helps in improving image clarity by modifying how pixel values contribute to the final reconstructed image. The process effectively sharpens the image and can enhance edges, making structures within the body more distinct.

For CT imaging, specific convolution algorithms are designed to handle the data that originates from the scanning process and can be tailored to either enhance certain aspects of the image or reduce noise and blurring. This makes convolution essential in optimizing the quality of CT images.

Other options, while relevant in the context of image processing, do not specifically relate to the process of reducing blurriness in CT images. Smoothing, for example, typically refers to reducing noise rather than focusing on sharpening. Segmentation involves dividing an image into segments for analysis, which does not address blurring. Filtering is a more general term that may include convolution but does not specifically imply the mathematical process discussed in the context of reducing blur in CT images.