Development and evaluation of a software tool for the generation of virtual liver lesions in multidetector-row CT datasets

• Published in: Academic radiology Vol. 20; no. 5; p. 614
• Main Authors: Karantzavelos, Konstantinos, Shin, Hoen-Oh, Jördens, Steffen, King, Benjamin, Ringe, Kristina, Hartung, Dagmar, Wacker, Frank, von Falck, Christian
• Format: Journal Article
• Language: English
• Published: United States 01.05.2013
• Subjects: Algorithms; Computer Simulation; Humans; Models, Biological; Observer Variation; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology Information Systems; Reproducibility of Results; Sensitivity and Specificity; Software; Tomography, X-Ray Computed - methods; User-Computer Interface
• ISSN: 1878-4046
• DOI: 10.1016/j.acra.2012.12.014

Development and evaluation of a software tool for the insertion of simulated hypodense liver lesions in multidetector-row computed tomography (CT) datasets. Forty software-generated hypodense liver lesions were inserted at random locations in 20 CT datasets by using the "alpha blending" technique and compared with 40 real metastatic lesions. The location, diameter (5-20 mm) and density of the simulated lesions were individually adjusted to closely resemble real lesions in each patient. Three blinded readers evaluated all 80 lesions twice in a 2-week interval using a five-point Likert confidence scale under standardized conditions. Nonparametric tests were used to statistically evaluate possible differences in scoring between real and simulated lesions. The correctness of the observer rating for real and simulated lesions was compared to chance distribution using the chi-squared statistics. The inter- and intraobserver variability was determined using Kendall's coefficient of concordance. The observer study did not reveal significant differences between the scoring for real versus simulated lesions for any of the readers (P > .05). The distribution of correct and false scoring of the lesions was not significantly different from chance distribution (P > .05). Inter- and intraobserver agreement was poor (Kendall W coefficient = 0.12/0.13). The proposed algorithm is suitable for creating realistic virtual liver lesions in CT datasets.