Generative deep-learning-model based contrast enhancement for digital subtraction angiography using a text-conditioned image-to-image model.

Journal: Computers in biology and medicine
Published Date: Jun 20, 2025

Abstract

BACKGROUND: Digital subtraction angiography (DSA) is an essential imaging technique in interventional radiology, enabling detailed visualization of blood vessels by subtracting pre- and post-contrast images. However, reduced contrast, either accidental or intentional, can impair the clarity of vascular structures. This issue becomes particularly critical in patients with chronic kidney disease (CKD), where minimizing iodinated contrast is necessary to reduce the risk of contrast-induced nephropathy (CIN). This study explored the potential of using a generative deep-learning-model based contrast enhancement technique for DSA.

Authors

  • Takeshi Takata
    Advanced Comprehensive Research Organization, Teikyo University, Tokyo, 173-0003, Japan. Electronic address: takata@med.teikyo-u.ac.jp.
  • Kentaro Yamada
    Department of Orthopedics, Tokyo Medical and Dental University, Tokyo, JPN.
  • Masayoshi Yamamoto
    Department of Radiology, Teikyo University School of Medicine, Tokyo, 173-8605, Japan. Electronic address: breguetjp@gmail.com.
  • Hiroshi Kondo
    Department of Radiology, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan.

Keywords

External Resources

Popular Topics
Recent Journals