To compare 2 different image creation/processing techniques during a standard CT scan in order to "see" problems in the liver and learn which method provides better image quality. The techniques use new artificial intelligence software to decrease image noise, which helps the radiologist to evaluate.
To evaluate whether post-processing software Adaptive Statistical Iterative Reconstruction (ASIR), ASIR-V, Veo 3.0 (GE version of Model-based Iterative Reconstruction (MBIR), and Deep Learning Image Reconstruction (DLIR) is able to preserve lesion detection in the liver and other measures of image quality at reduced radiation doses for computed tomography (CT).
Secondary Objectives:
Assessment of whether post-processing software enhances lesion detection in the liver and other measures of image quality at standard and reduced radiation doses.
Assessment of whether DLIR and GSI DLIR reconstructions perform differently, both in terms of accuracy and image quality metrics such as noise reduction.
Patient must be >/= 18 years of age and Men and non-pregnant women Pathology proven diagnosis of colon or colorectal carcinoma Liver metastases on most recent CT examination Standard of care CT abdomen examination planned WITH IV contrast
Exclusion Criteria:
Patients cannot give informed consent Patients cannot undergo CT examination
