Artifact suppression for breast specimen imaging in micro CBCT using deep learning.

Journal: BMC medical imaging
PMID: 38321390

Abstract

BACKGROUND: Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the acquisition scan time is to decrease of the number of projections, but this method generates streak artifacts on breast specimen images. Furthermore, the presence of a metallic-needle marker on a breast specimen causes metal artifacts that are prominently visible in the images. In this work, we propose a deep learning-based approach for suppressing both streak and metal artifacts in CBCT.

Authors

  • Sorapong Aootaphao
    Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand. aootaphao@gmail.com.
  • Puttisak Puttawibul
    Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand.
  • Pairash Thajchayapong
    National Science and Technology Development Agency, Pathum Thani, Thailand.
  • Saowapak S Thongvigitmanee
    Medical Imaging System Research Team, Assistive Technology and Medical Devices Research Group, National Electronics and Computer Technology Center, National Science and Technology Development Agency, Pathum Thani, Thailand.

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