Appreciating the critical step of feature-identification for enhanced

The synthetic mammogram is proving to be a valuable clinical tool [Durand 2018,

Ratanaprasatporn 2017]. In fact, the ability to generate a clinically-useful synthetic mammogram is considered to be a key step by which 3D mammography will replace standard 2D

mammography as the breast imaging tool of choice, since it would obviate the need to collect both 2D and 3D mammography scans for screening (see 2.6 The image processing chain that generates sDBT images and the value of the synthetic mammogram). Therefore, the

incorporation of a synthetic mammography capability into the sDBT system was identified as a key step to advance the clinical potential of the sDBT system and therefore considered a Specific Aim of this dissertation project (see 1.2 Purpose of this work, 1.3 Research questions asked in this work, and 4.4 Incorporating synthetic mammography into sDBT). As originally conceived,

potential uses. For example, compared to the full image stack, the synthetic mammogram could be compared more directly to previous standard mammograms in order to assess change. Also, since it summarized the findings distributed through the full image stack into a single image, it could provide an efficient reference, guiding readers to regions of concern in the full image stack. However, research over the past few years has demonstrated that the synthetic

mammogram has the potential to offer readers information with a higher diagnostic value than the standard mammogram[James 2018]. Improving the diagnostic value of the synthetic 2D image is accomplished by identifying features-of-interest in the 3D image stack and emphasizing these features in the synthetic image. In other words, the synthetic mammogram may be able to combine the benefits of decreased tissue overlap available in the 3D image stack with the efficiency of interpreting a 2D image. However, identifying and enhancing breast pathology in the x-ray image are difficult tasks, given the highly-variable appearance of diagnostically- important breast lesions. For example, masses and microcalcifications have very different image properties, including size and contrast, and often need to be distinguished from the dense and complex backgrounds in which they are located. As a result, numerous steps are required in the image processing chain that generates the synthetic mammogram. In fact, the algorithms developed for sDBT generate a set of synthetic images, each the result of a different image processing chain dedicated to the display of a specific type of pathology, such as the soft-tissue mass or microcalcification cluster. These processing chains incorporate combinations of

Laplacian decomposition, weighted forward projection, and weighted recombination customized to sDBT to generate the synthetic image (see 4.4 Incorporating synthetic mammography into sDBT). Testing to date using objective measures of signal intensity and feature detectability suggest that these sDBT synthetic images will be useful (see 4.4.2 Phantom-based testing of a

forward projection approach incorporating feature enhancement). However, reader study of patient images will be required to determine their actual clinical value (see 4.4.4 The clinical utility of sDBT-generated synthetic mammograms compared to standard mammography). Several vendors offer the option to generate synthetic mammograms, including Hologic (C- View), GE (V-Preview), and Siemens (Insight) [Durand 2018]. Each is proprietary and yields images that emphasize different characteristics. As such, the clinical value of each differs, and the development of computer algorithms to generate synthetic images from the information collected by a DBT study remains a rapidly-evolving area of quite active research [Geras 2019, James 2018]. In large part, this research is focused on improving the detection of features in the 3D image space, so that diagnostically-important features are most-importantly (1) not missed, (2) accurately displayed, and (3) perhaps even enhanced in the synthetic image. Indeed, for all image processing approaches designed to produce feature-enhanced synthetic images, the accuracy of the feature-detection step is key.

Recent experience with standard 2D mammography as well as moving-source DBT has suggested that deep learning algorithms have the potential to improve the accuracy of feature identification [James 2018, Rodriguez-Ruiz 2018, Rodriguez-Ruiz 2019, Geras 2019]. GE offers a CAD-enhanced synthetic mammography option (Enhanced V-Preview) that identifies up to five suspicious soft tissue lesions in the 3D image stack and enhances the lesions in the synthetic image [iCAD 2017]. Hologic has developed an artificial intelligence-based approach to generate 6-mm-thick “SmartSlices,” in order to reduce the number of images and storage space

requirements of the 3D stack while maintaining important feature display [Hologic 2020]. The image processing chain developed for sDBT in this dissertation work should accommodate a deep learning feature-detection algorithm. However, such networks require extensive training.

As such, their development is dependent on the availability of a library of annotated images displaying a broad range of pathology. Building such a library of actual patient images would require many years of ongoing clinical experience, which should be considered an important step for sDBT. However, recent advances in the generation of images using a virtual DBT model have suggested the opportunity to generate image databases through virtual clinical studies for algorithm training [Badano 2018]. Computer training based on a virtual sDBT system and a collection of virtual sDBT images would greatly accelerate the development of image processing for the sDBT system and should be considered as a future direction for research with this

experimental technology.