title: Use of a Noise Optimized Monoenergetic Algorithm for Patient-Size Independent Selection of an Optimal Energy Level During Dual-Energy CT of the Pancreas.
abstract: OBJECTIVE
  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.
  
  
  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.
authors: From the *Department of Radiology, Duke University Medical Center, Durham, NC; †Siemens Medical Solutions USA Inc., Pleasanton, CA; and ‡Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC.
authors: Davide
authors: D
authors: Bellini
authors: Sonia
authors: S
authors: Gupta
authors: Juan Carlos
authors: JC
authors: Ramirez-Giraldo
authors: Wanyi
authors: W
authors: Fu
authors: Sandra S
authors: SS
authors: Stinnett
authors: Bhavik
authors: B
authors: Patel
authors: Achille
authors: A
authors: Mileto
authors: Daniele
authors: D
authors: Marin
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D049628:Body Size; D006801:Humans; D008875:Middle Aged; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging* / Q000503:physiopathology; D011829:Radiation Dosage; D000069079:Radiation Exposure / Q000032:analysis / Q000517:prevention & control*; D011835:Radiation Protection / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods; D015900:Radiography, Dual-Energy Scanned Projection / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods*