RATIONALE AND OBJECTIVES: Development and evaluation of a software tool for the insertion of simulated hypodense liver lesions in multidetector-row computed tomography (CT) datasets. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: The proposed algorithm is suitable for creating realistic virtual liver lesions in CT datasets.
RATIONALE AND OBJECTIVES: Development and evaluation of a software tool for the insertion of simulated hypodense liver lesions in multidetector-row computed tomography (CT) datasets. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: The proposed algorithm is suitable for creating realistic virtual liver lesions in CT datasets.
Authors: Brendan L Eck; Rachid Fahmi; Kevin M Brown; Stanislav Zabic; Nilgoun Raihani; Jun Miao; David L Wilson Journal: Med Phys Date: 2015-10 Impact factor: 4.071
Authors: Baiyu Chen; Chi Ma; Shuai Leng; Jeff L Fidler; Shannon P Sheedy; Cynthia H McCollough; Joel G Fletcher; Lifeng Yu Journal: Acad Radiol Date: 2016-07-16 Impact factor: 3.173
Authors: Christian von Falck; Vesela Bratanova; Thomas Rodt; Bernhard Meyer; Stephan Waldeck; Frank Wacker; Hoen-oh Shin Journal: PLoS One Date: 2013-02-13 Impact factor: 3.240