Virtual assessment of stereoscopic viewing of digital breast tomosynthesis projection images

Abstract

Digital breast tomosynthesis (DBT) acquires a series of projection images from different angles as an x-ray source rotates around the breast. Such imaging geometry lends DBT naturally to stereoscopic viewing as two projection images with a reasonable separation angle can easily form a stereo pair. This simulation study assessed the efficacy of stereo viewing of DBT projection images. Three-dimensional computational breast phantoms with realistically shaped synthetic lesions were scanned by three simulated DBT systems. The projection images were combined into a sequence of stereo pairs and presented to a stereomatching-based model observer for deciding lesion presence. Signal-to-noise ratio was estimated, and the detection performance with stack viewing of reconstructed slices was the benchmark. We have shown that: (1) stereo viewing of projection images may underperform stack viewing of reconstructed slices for current DBT geometries; (2) DBT geometries may impact the efficacy of the two viewing modes differently: narrow-arc and wide-arc geometries may be better for stereo viewing and stack viewing, respectively; (3) the efficacy of stereo viewing may be more robust than stack viewing to reductions in dose. While in principle stereo viewing is potentially effective for visualizing DBT data, effective stereo viewing may require specifically optimized DBT image acquisition.

The Effect of Stereoscopic Three-Dimensional Images on Vocabulary Learning

“…While testing the effectiveness of alternative visuals, such as line drawings (Dwyer, 1969), animated images (Lowe, 1999), and photographs (Mayer & Sims, 1994), has received considerable attention, empirical research on the use of NS3D and S3D images in education is limited to studies involving spatial learning (McIntire, Havig, & Geiselman, 2014). Some examples of domains where research has been conducted on depth-related tasks using S3D technology are surgery (Lewis, Zaritsky, Heinrichs, & Nezhat, 2006), imaging (Wen, Chang, Reinhold, Lo, & Markey, 2018), and teleoperation (Drascic, 1991). Studies using S3D images for nonspatial learning tasks are much less common.…”

Model observer for assessing digital breast tomosynthesis for multi-lesion detection in the presence of anatomical noise

“…shape of breast, and texture within each breast), and the random structural variations in the breast seen among actual patients are needed to evaluate task-based image quality (e.g. Bakic et al (2011), Kiarashi et al (2012), Hsu et al (2013), Reinhold et al (2017) and Wen et al (2018)). We used Bakic et al's 3D computational breast phantom (Bakic et al 2011), which uses a region growing method to simulate basic breast anatomical components including skin, adipose tissue, glandular tissue, and Cooper's ligaments.…”