The use of conventional clinical trials to optimise technology and techniques in breast cancer screening carries with it issues of dose, high cost and delay. This has motivated the development of Virtual Clinical Trials (VCTs) as an alternative in-silico assessment paradigm. However, such an approach requires a set of modelling tools that can realistically represent the key biological and technical components within the imaging chain. The OPTIMAM image simulation toolbox provides a complete validated end-to-end solution for VCTs, wherein commonly-found regular and irregular lesions can be successfully and realistically simulated. As spiculated lesions are the second most common form of solid mass we report on our latest developments to produce realistic spiculated lesion models, with particular application in Alternative Forced Choice trials. We make use of sets of spicules drawn using manually annotated landmarks and interpolated by a fitted 3D spline for each spicule. Once combined with a solid core, these are inserted into 2D and tomosynthesis image segments and blended using a combination of elongation, rotational alignment with background, spicule twisting and core radial contraction effects. A mixture of real and simulated images (86 2D and 86 DBT images) with spiculated lesions were presented to an experienced radiologist in an observer study. The latest observer study results demonstrated that 88.4% of simulated images of lesions in 2D and 67.4% of simulated lesions in DBT were rated as definitely or probably real on a six-point scale. This presents a significant improvement on our previous work which did not employ any background blending algorithms to simulate spiculated lesions in clinical images.
Premkumar Elangovan, Elena Mihalas, Majdi Alnowami, Kenneth C. Young, David R. Dance, Victoria Cooke, Louise Wilkinson, Rosalind M. Given-Wilson, Matthew G. Wallis, and Kevin Wells, "Design and validation of biologically inspired spiculated breast lesion models utilizing structural tissue distortion," Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105730B (Presented at SPIE Medical Imaging: February 12, 2018; Published: 9 March 2018); https://doi.org/10.1117/12.2293421.
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