Coronary artery disease (CAD) is one of the leading causes of death world-wide. Because of its great clinical importance, technological and diagnostic advances occur to combat it at a similarly high rate. With the number of clinical trials that would be necessary, this becomes infeasible and virtual clinical trials (VCT) are necessary. These require virtual patients and virtual pathologies. A generative adversarial network (GAN) was used to create a library of coronary plaques which were physiologically validated with finite element analysis. The resulting plaque library consists of a large number of realistic, variable virtual pathologies for use in VCTs.
The purpose of this work was to estimate bias in measuring the size of spherical and non-spherical lesions by
radiologists using three sizing techniques under a variety of simulated lesion and reconstruction slice thickness
conditions. We designed a reader study in which six radiologists estimated the size of 10 synthetic nodules of various
sizes, shapes and densities embedded within a realistic anthropomorphic thorax phantom from CT scan data. In this
manuscript we report preliminary results for the first four readers (Reader 1-4). Two repeat CT scans of the phantom
containing each nodule were acquired using a Philips 16-slice scanner at a 0.8 and 5 mm slice thickness. The readers
measured the sizes of all nodules for each of the 40 resulting scans (10 nodules x 2 slice thickness x 2 repeat scans)
using three sizing techniques (1D longest in-slice dimension; 2D area from longest in-slice dimension and corresponding
longest perpendicular dimension; 3D semi-automated volume) in each of 2 reading sessions. The normalized size was
estimated for each sizing method and an inter-comparison of bias among methods was performed. The overall relative
biases (standard deviation) of the 1D, 2D and 3D methods for the four readers subset (Readers 1-4) were -13.4 (20.3),
-15.3 (28.4) and 4.8 (21.2) percentage points, respectively. The relative biases for the 3D volume sizing method was
statistically lower than either the 1D or 2D method (p<0.001 for 1D vs. 3D and 2D vs. 3D).
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