UC Davis

Breast computed tomography (CT) is a relatively new breast imaging modality based upon cone-beam CT geometry, and there are ongoing efforts to optimize breast CT for the detection of breast cancer lesions. Clinical trials are considered the ideal approach when conducting protocol optimization studies. However, the time, expense, and extensive database required to conduct these studies pose significant challenges and costs. As an alternative to clinical studies, simulation studies have been proposed, where synthetic images are generated using phantom imaging or computer simulations. In addition, model observers have been proposed in lieu of human observers to evaluate the simulated images. By using simulated images and model observers, thousands of images can be generated and evaluated in a short period of time. In this work, we developed methods to simulate “hybrid” breast CT images, where mathematically generated mass lesions and microcalcifications are inserted into actual patient breast CT volume data sets. Then, we used model observers, namely the pre-whitened matched filter (PWMF) and convolutional neural networks (CNNs), to detect the simulated lesions.First, we simulated contrast-enhanced mass lesions and explored the improvement in mass lesion detectability due to contrast enhancement across lesion diameter, section thickness, breast density, and view plane. An average 20% improvement was observed, and a larger improvement was observed for patients with dense breasts. Small lesions are generally harder to detect in dense breasts, but these results demonstrated that injected contrast can substantially improve detection performance in dense breasts. Next, we compared the PWMF model observer with the CNN model observer for detecting mathematically generated unenhanced mass lesions inserted into 1) breast CT background and 2) Gaussian background, where the PWMF is known to be an ideal observer. In Gaussian background, the CNN performed essentially identically to the PWMF across lesion sizes and section thicknesses. In breast CT background, the CNN outperformed the PWMF across lesion size, breast density, and most section thicknesses. These findings suggest that in breast CT images, CNNs capture more diagnostic information than PWMFs and may be a more pertinent observer when conducting optimal performance studies. Lastly, we simulated microcalcifications and microcalcification clusters. The loss of intensity owing to partial volume effects was modeled and used to mathematically insert microcalcifications into acquired patient breast CT images. 2D and 3D CNNs were used to evaluate the detectability of simulated calcifications across clinical parameters. Our results demonstrated the utility of the maximum intensity projection (MIP) for displaying image volumes containing microcalcification clusters. We found that there was no statistically significant difference in detection performance when using the MIP compared to all slices in the native section thickness, but that thicker sections led to reduced detection performance. The MIP procedure essentially compresses 3D images to 2D images, resulting in efficient and better detection for microcalcifications. Collectively, these studies elucidate the key factors affecting mass lesion and microcalcification detectability in unenhanced and contrast-enhanced breast CT and demonstrate the utility of model observers for examining breast CT images when human observers are unavailable. As breast CT advances towards translation to the clinic, these studies will be useful for optimizing breast CT protocols.