DeLeMA CT

Aim

In cerebral computed tomography (CT), it is important to acquire images with sufficiently low noise level, allowing the radiologist to distinguish between grey and white matter. In the presence of metal such as aneurism clips, image artifacts may deteriorate image quality. Metal artifact reduction methods such as dual energy CT with virtual monochromatic images and metal artifact reduction software (MAR) can reduce artifacts, but it is unclear how those methods work when combined with novel deep learning image reconstruction (DLIR) algorithms.

This experimental phantom study aimed to assess the effect of DLIR in combination with MAR and Dual energy CT.

Participants

• Department of Radiology, OUH
• Department of Neurosurgery, OUH
• Faculty of Health Sciences, Oslo Metropolitan University, Norway