Colon Cancer Clinical Trial
Lesion Detection Assessment in the Liver: Standard vs Low Radiation Dose Using Varied Post-Processing Techniques
Summary
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.
Full Description
Primary Objective:
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.
Eligibility Criteria
Inclusion Criteria:
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
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There is 1 Location for this study
Houston Texas, 77030, United States
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