title: Evaluation of non-linear blending in dual-energy computed tomography.
abstract: Dual-energy CT scanning has significant potential for disease identification and classification. However, it dramatically increases the amount of data collected and therefore impacts the clinical workflow. One way to simplify image review is to fuse CT datasets of different tube energies into a unique blended dataset with desirable properties. A non-linear blending method based on a modified sigmoid function was compared to a standard 0.3 linear blending method. The methods were evaluated in both a liver phantom and patient study. The liver phantom contained six syringes of known CT contrast which were placed in a bovine liver. After scanning at multiple tube currents (45, 55, 65, 75, 85, 95, 105, and 115 mAs for the 140-kV tube), the datasets were blended using both methods. A contrast-to-noise (CNR) measure was calculated for each syringe. In addition, all eight scans were normalized using the effective dose and statistically compared. In the patient study, 45 dual-energy CT scans were retrospectively mixed using the 0.3 linear blending and modified sigmoid blending functions. The scans were compared visually by two radiologists. For the 15, 45, and 64 HU syringes, the non-linear blended images exhibited similar CNR to the linear blended images; however, for the 79, 116, and 145 HU syringes, the non-linear blended images consistently had a higher CNR across dose settings. The radiologists qualitatively preferred the non-linear blended images of the phantom. In the patient study, the radiologists preferred non-linear blending in 31 of 45 cases with a strong preference in bowel and liver cases. Non-linear blending of dual energy data can provide an improvement in CNR over linear blending and is accompanied by a visual preference for non-linear blended images. Further study on selection of blending parameters and lesion conspicuity in non-linear blended images is being pursued.
authors.affiliation: Biomedical Imaging Resource, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, United States. holmes.david3@mayo.edu
authors.forename: David R
authors.initials: DR
authors.lastname: Holmes
authors.forename: Joel G
authors.initials: JG
authors.lastname: Fletcher
authors.forename: Anja
authors.initials: A
authors.lastname: Apel
authors.forename: James E
authors.initials: JE
authors.lastname: Huprich
authors.forename: Hassan
authors.initials: H
authors.lastname: Siddiki
authors.forename: David M
authors.initials: DM
authors.lastname: Hough
authors.forename: Bernhard
authors.initials: B
authors.lastname: Schmidt
authors.forename: Thomas G
authors.initials: TG
authors.lastname: Flohr
authors.forename: Richard
authors.initials: R
authors.lastname: Robb
authors.forename: Cynthia
authors.initials: C
authors.lastname: McCollough
authors.forename: Michael
authors.initials: M
authors.lastname: Wittmer
authors.forename: Christian
authors.initials: C
authors.lastname: Eusemann
mesh_terms: D000465:Algorithms; D000818:Animals; D002417:Cattle; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging*; D017711:Nonlinear Dynamics; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*