Use of a Noise Optimized Monoenergetic Algorithm for Patient-Size Independent Selection of an Optimal Energy Level During Dual-Energy CT of the Pancreas. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Bellini, Davide; Gupta, Sonia; Ramirez-Giraldo, Juan Carlos; Fu, Wanyi; Stinnett, Sandra S.; Patel, Bhavik; Mileto, Achille; Marin, Daniele
• Is Part Of: Journal of computer assisted tomography. Volume 41:Issue 1(2017)
• Publication Date: 2017-01
• Subjects: dual-energy multidetector CT -- pancreas -- monoenergetic images -- image quality -- patient body habitus; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000000492
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract : Purpose: To investigate the impact of a second-generation noise-optimized monoenergetic algorithm on selection of the optimal energy level, image quality, and effect of patient body habitus for dual-energy multidetector computed tomography of the pancreas. Materials and Methods: Fifty-nine patients (38 men, 21 women) underwent dual-energy multidetector computed tomography (80/Sn140 kV) in the pancreatic parenchymal phase. Image data sets, at energy levels ranging from 40 to 80 keV (in 5-keV increments), were reconstructed using first-generation and second-generation noise-optimized monoenergetic algorithm. Noise, pancreatic contrast-to-noise ratio (CNRpancreas ), and CNR with a noise constraint (CNRNC ) were calculated and compared among the different reconstructed data sets. Qualitative assessment of image quality was performed by 3 readers. Results: For all energy levels below 70 keV, noise was significantly lower ( P ⩽ 0.05) and CNRpancreas significantly higher ( P < 0.001), with the second-generation monoenergetic algorithm. Furthermore, the second-generation algorithm was less susceptible to variability related to patient body habitus in the selection of the optimal energy level. The maximal CNRpancreas occurred at 40 keV in 98% (58 of 59) of patients with the second-generation monoenergetic algorithm. However, the CNRNC and readers' image quality scores showed that, even with a second-generation monoenergetic algorithm, higher reconstructed energy levels (60–65 keV) represented the optimal energy level. Conclusions: Second-generation noise-optimized monoenergetic algorithm can improve the image quality of lower-energy monoenergetic images of the pancreas, while decreasing the variability related to patient body habitus in selection of the optimal energy level.