  Imaging phantoms are test patterns used to measure image quality in computer
tomography (CT) systems. A new phantom platform (Mercury Phantom, Gammex)
provides test patterns for estimating the task transfer function (TTF) or noise
power spectrum (NPF) and simulates different patient sizes. Determining which
image slices are suitable for analysis currently requires manual annotation of
these patterns by an expert, as subtle defects may make an image unsuitable for
measurement. We propose a method of automatically classifying these test
patterns in a series of phantom images using deep learning techniques. By
adapting a convolutional neural network based on the VGG19 architecture with
weights trained on ImageNet, we use transfer learning to produce a classifier
for this domain. The classifier is trained and evaluated with over 3,500
phantom images acquired at a university medical center. Input channels for
color images are successfully adapted to convey contextual information for
phantom images. A series of ablation studies are employed to verify design
aspects of the classifier and evaluate its performance under varying training
conditions. Our solution makes extensive use of image augmentation to produce a
classifier that accurately classifies typical phantom images with 98% accuracy,
while maintaining as much as 86% accuracy when the phantom is improperly
imaged.
