Accuracy assessment and characterization of x-ray coded aperture coherent scatter spectral imaging for breast cancer classification.

Published

Journal Article

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 in ex vivo breast 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 within ex vivo samples. Furthermore, the results indicate potential CACSSI imaging system configurations for implementation in subsequent imaging development studies.

Full Text

Duke Authors

Cited Authors

  • Lakshmanan, MN; Greenberg, JA; Samei, E; Kapadia, AJ

Published Date

  • January 2017

Published In

Volume / Issue

  • 4 / 1

Start / End Page

  • 013505 -

PubMed ID

  • 28331884

Pubmed Central ID

  • 28331884

International Standard Serial Number (ISSN)

  • 2329-4302

Digital Object Identifier (DOI)

  • 10.1117/1.JMI.4.1.013505

Language

  • eng

Conference Location

  • United States