Abstract
Background: Peristalsis-related streak artifacts on the liver compromise image quality and diagnostic accuracy. Purpose: To assess dual-layer spectral-detector computed tomography (CT) image reconstructions for reducing intestinal peristalsis-related streak artifacts on the liver. Methods: We retrospectively evaluated 220 contrast-enhanced abdominal dual-energy CT scans in 131 consecutive patients (mean age: 68 ± 10 years, 120 men) who underwent routine clinical dual-layer spectral-detector CT imaging (120 kVp, 40 keV, 200 keV, virtual non-contrast (VNC), iodine images). Two independent readers evaluated bowel peristalsis streak artifacts on the liver qualitatively on a five-point Likert scale (1 = none to 5 = severe) and quantitatively by depth of streak artifact extension into the liver and measurements of Hounsfield Unit and iodine concentration differences from normal liver. Artifact severity between image reconstructions were compared by Wilcoxon signed-rank and paired t-tests. Results: 12 scans were excluded due to missing spectral data, artifacts on the liver originating from metallic foreign materials, or oral contrast material. Streak artifacts on the liver were seen in 51/208 (25%) scans and involved the left lobe only in 49/51 (96%), the right lobe only in 0/51 (0%), and both lobes in 2/51 (4%) scans. Artifact frequency was lower in iodine than in 120 kVp images (scans 18/208 vs. 51/208, p < 0.001). Artifact severity was less in iodine than in 120 kVp images (median score 1 vs. 3, p < 0.001). Streak artifact extension into the liver was shorter in iodine than 120 kVp images (mean length 2 ± 4 vs. 12 ± 5 mm, p < 0.001). Hounsfield Unit and iodine concentration differed significantly between bright streak artifacts and normal liver in 120 kVp, 40 keV, 200 keV, and VNC images (p < 0.001, each), but not in iodine images (p = 0.23). Conclusion: Intestinal peristalsis-related streak artifacts commonly affect the left liver lobe at CT and can be substantially reduced by viewing iodine dual-energy CT image reconstructions.
Funder
National Institutes of Health
Philips
Cited by
3 articles.
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