Asymmetric scatter kernel estimation neural network for digital breast tomosynthesis.

Journal: Journal of medical imaging (Bellingham, Wash.)
Published Date: Jun 12, 2025

Abstract

PURPOSE: Various deep learning (DL) approaches have been developed for estimating scatter radiation in digital breast tomosynthesis (DBT). Existing DL methods generally employ an end-to-end training approach, overlooking the underlying physics of scatter formation. We propose a deep learning approach inspired by asymmetric scatter kernel superposition to estimate scatter in DBT.

Authors

  • Subong Hyun
    KAIST, Department of Nuclear and Quantum Engineering, Daejeon, Republic of Korea.
  • Seoyoung Lee
    Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
  • Ilwong Choi
    DRTECH Corp., Research & Development Center, Seongnam, Republic of Korea.
  • Choul Woo Shin
    DRTECH Corp., Research & Development Center, Seongnam, Republic of Korea.
  • Seungryong Cho

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