Accuracy assessment and characterization of x-ray coded aperture coherent scatter spectral imaging for breast cancer classification.
Although transmission-based x-ray imaging is the most commonly used imaging approach for breast cancer detection, it exhibits false negative rates higher than 15%. To improve cancer detection accuracy, x-ray coherent scatter computed tomography (CSCT) has been explored to potentially detect cancer with greater consistency. However, the 10-min scan duration of CSCT limits its possible clinical applications. The coded aperture coherent scatter spectral imaging (CACSSI) technique has been shown to reduce scan time through enabling single-angle imaging while providing high detection accuracy. Here, we use Monte Carlo simulations to test analytical optimization studies of the CACSSI technique, specifically for detecting cancer inex vivobreast samples. An anthropomorphic breast tissue phantom was modeled, a CACSSI imaging system was virtually simulated to image the phantom, a diagnostic voxel classification algorithm was applied to all reconstructed voxels in the phantom, and receiver-operator characteristics analysis of the voxel classification was used to evaluate and characterize the imaging system for a range of parameters that have been optimized in a prior analytical study. The results indicate that CACSSI is able to identify the distribution of cancerous and healthy tissues (i.e., fibroglandular, adipose, or a mix of the two) in tissue samples with a cancerous voxel identification area-under-the-curve of 0.94 through a scan lasting less than 10 s per slice. These results show that coded aperture scatter imaging has the potential to provide scatter images that automatically differentiate cancerous and healthy tissue withinex vivosamples. Furthermore, the results indicate potential CACSSI imaging system configurations for implementation in subsequent imaging development studies.
National Institutes of Health Clinical Center , Department of Radiology and Imaging Sciences, Bethesda, Maryland, United States.
Manu N
MN
Lakshmanan
Duke University , Department of Electrical and Computer Engineering, Durham, North Carolina, United States.
Joel A
JA
Greenberg
Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States; Duke University Medical Center, Ravin Advanced Imaging Labs, Durham, North Carolina, United States; Duke University, Department of Physics, Durham, North Carolina, United States.
Ehsan
E
Samei
Duke University Medical Center, Ravin Advanced Imaging Labs, Durham, North Carolina, United States; Duke University, Department of Physics, Durham, North Carolina, United States.
Anuj J
AJ
Kapadia
breast cancer; characterization; coded aperture; receiver-operator characteristics; x-ray coherent scatter imaging