Objective: To evaluate the clinical value of dual-energy spectral CT with adaptive statistical iterative reconstruction (ASiR) for reducing contrast medium dose in CT portal venography (CTPV). Methods: This prospective study was institutional review board-approved, and written informed consent was obtained from all patients. 50 patients undergoing abdominal CT were randomized to 2 groups: Group A (n 5 25), using spectral CT and 350 mgI kg 21 contrast injection protocol; Group B (n 5 25), using standard 120 kVp and 500 mgI kg 21 contrast. Spectral CT images at 60 keV and standard 120-kVp images were both reconstructed with 50% ASiR. CT number and contrastto-noise ratio (CNR) for intrahepatic and extrahepatic portal veins were measured. The maximum intensity projection (MIP) and volume-rendering (VR) images were used for subjective evaluation. These two kinds of results were statistically analyzed. Results: CNR values for the intrahepatic portal vein of the 60-keV spectral images (4.2 6 1.1) were higher than those of 120-kVp images (3.0 6 2.1) (p 5 0.03) and were the same for the extrahepatic portal vein (5.9 6 1.4 vs 5.9 6 1.6, p 5 0.90). The portal vein and left and right branches in the 60-keV spectral images had higher CT number and lower standard deviation than the 120-kVp images (p , 0.05). Radiation dose (dose-length product and effective dose) and subjective image quality were similar for the two groups, while the spectral CT group required 25% less iodine dose (23.1 6 3.2 g vs 30.5 6 5.0 g). Conclusion:The 60-keV spectral CT images with ASiR allow 25% reduction in the iodine dose while providing better or equal image quality as the standard 120-kVp images in portal venography with comparable radiation dose. Advances in knowledge: Compared with conventional 120-kVp CT, the use of 60-keV spectral CT images provides 25% contrast dose reduction with similar image quality in CTPV. Compared with conventional 120-kVp CT, the use of 60-keV spectral CT images with ASiR algorithm improves CNR values for the intrahepatic portal vein.

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The public has been increasingly concerned about radiation exposure. Medical radiation exposure is no exception 1 . CT has become more prevalent. Meanwhile, the dose received by the patient has increased. A survey shows that medical exposure constituted nearly half of the total radiation exposure of the US population from all sources in 2006, and CT was a major contributor. 2 As a non-invasive means of inspection, CT and MRI are the two major imaging modalities and have almost replaced exploratory laparotomy for diagnosing abdominal problems. Even though MRI has the advantage of no radiation dose issue, it is more expensive than CT and has contraindications for certain patients, e.g. patients with claustrophobia or foreign metal objects in the body. So CT examinations are still widely used and there is great desire to reduce radiation dose for CT owing to the potential cancer risk caused by CT radiation. 3

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Objectives To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low‐dose chest CT in comparison with 40% adaptive statistical iterative reconstruction‐Veo (ASiR‐V40%) algorithm. Methods This retrospective study included 86 patients who underwent low‐dose CT for lung cancer screening. Images were reconstructed with ASiR‐V40% and DLIR at low (DLIR‐L), medium (DLIR‐M), and high (DLIR‐H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures. Results The effective dose was 1.03 ± 0.36 mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5 HU among the four reconstructions. Compared with ASiR‐V40%, the DLIR‐L, DLIR‐M, and DLIR‐H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all p < 0.001). ASiR‐V40%, DLIR‐L, and DLIR‐M had equivalent score in visualizing small structures (all p > 0.05), whereas DLIR‐H had slightly lower score. Conclusions Compared with ASiR‐V40%, DLIR significantly reduces image noise in low‐dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application.