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Training of deep learning-based segmentation algorithms requires large datasets of annotated images. Obtaining large clinical datasets, particularly for interventional imaging, is difficult and annotating vessels manually in the images is very time-consuming. Simulated images can be used to supplement clinical datasets. We developed a technique for the simulation of realistic, anatomically- and physiologically-motivated hepatic artery trees and subsequent synthesis of fluoroscopic images. The proposed approach creates a network of main feeding arteries for each of the eight liver segments as defined by the Couinaud classification system. A constrained constructive optimization based approach was then used to connect a set of randomly generated endpoints within each segment to the corresponding feeding artery. Vessel curvature was created using cubic splines and the generated vasculature was inserted into the digital XCAT phantom. The simulated 2D fluoroscopic images were generated using ray tracing and included focal spot blur, detector blur and Poisson noise. The length ratio (1.1 ± 1.7) and two parameters from Murray’s law, branching angles (7.9 ± 7.7𝑜 mean absolute difference from Murray’s law) and radius ratio (1.0 ± 0.1) of the generated vasculature were in accordance with values reported in literature (1.3, Murray’s law applied to branching angles, and 1.0 respectively). Simulated vasculature included main branches for each of the eight Couinaud segments, where 87% of all connected endpoints terminated in the same segment. Simulated 2D projection images were analyzed using a vessel phantom study with contrast-enhanced tubes (0.305-3.353 mm diameter). The normalized root mean squared difference between the measured and simulated vessel profiles averaged 3.5%. In conclusion, the proposed method provides realistic simulated fluoroscopic images of the liver vasculature and could prove useful for the training of machine learning based algorithms for vessel segmentation.
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