Full Title: Deep learning image reconstruction with and without metal artifact reduction in Head CT
Start: January 2023
End: January 2024
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 aims to assess the effect of DLIR in combination with MAR and Dual energy CT.
- Department of Radiology, OUH
- Department of Neurosurgery, OUH
- Faculty of Health Sciences, Oslo Metropolitan University, Norway
Associate Professor, Research Radiographer, PhD
Department of Radiology
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