Anthropomorphic ResNet18 for multi-vendor DBT image quality evaluation

Purpose: This work aims to develop an anthropomorphic convolutional neural network (CNN) classifier, based on the ResNet18 deep learning network and validate it for task based image quality evaluation of digital breast tomosynthesis (DBT) using a structured phantom with non-spiculated mass simulating lesions. Methods: The phantom is constructed from an acrylic breast-shaped container, filled with acrylic spheres and water resembling the background. Five 3D printed non-spiculated mass targets are also inserted in the phantom each with differing size from 1.5mm to 5.7mm. The phantom was scanned 530 times on 8 different DBT systems with 3 dose levels. Half of the image dataset was read by human readers in 4-alternative forced choice (4-AFC) paradigm. The 4-AFC human scores were used to label the cropped signal present and signal absent images. A pre-trained ResNet18 neural network was used and modified for binary classification and the labeled images were used to further train the network for the specific non-spiculated mass detection task. With completed 50 training epochs, the resulting ResNet18 classifier was validated wit the second half of the image dataset against human results. During the training process the loss and accuracy were stored, and statistical analysis was performed for the validation of the ResNet18 against human observers. Results and conclusions: The ResNet18 classifier shows good agreement against human observers for most of the DBT systems and reading sessions. The overall correlation was higher than 0.92. The study shows that a CNN can successfully approximate human scores and can be used for future DBT system image quality estimation studies.