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.
Since its introduction in 1972, computed tomography (CT) has become an indispensable diagnostic tool of modern medicine. Its use has increased rapidly, especially during the last two decades, mainly driven by an enormous technical development that continuously broadens its diagnostic possibilities . State-of-the-art multidetector CT (MDCT) scanners allow the routine acquisition of nearly isotropic datasets with submillimeter voxel dimensions at a comfortable breathhold of less than 10 seconds. To manage the resultant increasing amount of image data, computer-based visualization and analysis tools have been developed that aim at supporting radiologists . Computer-aided detection (CAD) algorithms in chest CT , virtual colonoscopy , and breast diagnostics are even intended to act as a second independent reader to improve diagnostic accuracy. Other tools assist liver imaging by providing means for automated lesion detection and characterization as well as lesion quantification during longitudinal follow-up . These tools need to be carefully evaluated before they can be introduced into routine clinical use. Therefore, large well-defined lesion databases are desirable which are time-consuming and cost-intensive to develop. Computer-generated software phantoms have been proposed to facilitate the systematic testing and validation of postprocessing tools and they have been successfully used in chest CT . Because soft-tissue organs such as the liver are frequently affected by metastatic disease and lesion size is regarded as a surrogate marker of tumor response during longitudinal follow-up, a comprehensive database of virtual lesions is of potential benefit as well .
Therefore, the aim of our study was to develop a software tool for the creation of virtual liver lesions and to evaluate whether these simulated lesions are distinguishable from real lesions by human readers.
Materials and methods
Software Phantom
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C=αAA+(1−αAA)B C
=
α
A
A
+
(
1
−
α
A
A
)
B
where A is the density value of the lesion to be inserted, α__A is the opacity of A , B is the density value of the background, and C is the resulting density value.
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Phantom Evaluation
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Statistical Analysis
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Results
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Discussion
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Conclusion
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