Virtual clinical trials using inserted pathology in clinical images: investigation of assumptions for local glandularity and noise

Virtual clinical trials have been proposed as a viable alternative to clinical trials for testing and comparing the performance of breast imaging systems. One of the main simulation methodologies used in virtual trials employs clinical images of patients in which simulated models of cancer are inserted using a physics-based template multiplication technique. The purpose of this work is to investigate two assumptions commonly considered in this simulation approach: Firstly, given the absence of useful depth information in a clinical situation, an average measure of the local breast glandularity is commonly used as an estimate of the breast composition at the insertion site; secondly, it is also assumed that any change in the relative noise in the image at the insertion site, after insertion of a mass, is negligible. In order to test the validity of these assumptions, spheres representing idealised masses and anthropomorphic computational breast phantoms with perfect prior knowledge of local tissue composition and distribution were used. Results from several region of interest (ROI) insertions demonstrated a lack of variation obtained in contrast with insertion depth using the template multiplication insertion method as compared to the true depth-wise variation contrast values obtained from voxel replacement in a heterogeneous phantom. It was also found that the amount of noise is underestimated by insertion of spherical masses using template multiplication method by 8% - 29% compared to voxel replacement for the test conditions. This resulted in up to 12% variation in contrast-to-noise-ratio (CNR) values between template multiplication and voxel replacement methods.