Question

What does convolution do to the image quality when it is added to the back projection reconstruction process?

1) normalizes the attenuation coefficients to ensure tissues are displayed with correct CT numbers
2) increases the number of projection thereby reducing streaking artifacts.
3) improves contrast resolution by reducing scatter radiation
4) enhances important characteristics of attenuation profiles thus reducing streak and star artifact

Answer 1

Convolution in image processing, particularly in CT scan reconstruction, enhances important image characteristics, which reduces artifacts like streaks and stars, thereby improving image quality and contrast resolution.

Step-by-step explanation:

Convolution is a mathematical operation used in image processing to enhance important characteristics of attenuation profiles in imaging methods such as CT (computed axial tomography) scanning. By applying a convolution kernel to the back projection reconstruction process, it helps reduce streak and star artifacts, which are common issues in the reconstructed images. This improves the image quality by sharpening image features and making the underlying structures more distinguishable. While the convolution process does not directly increase the number of projections or normalize attenuation coefficients, nor does it reduce scatter radiation, it substantially improves the contrast resolution in the resultant images, which is crucial for accurate diagnosis and interpretation. Techniques like T1, T2, and proton density scans in MRI, as well as SPECT and PET scans in nuclear medicine, also utilize various physical principles to enhance image contrast for more informative diagnosis.