Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Medical Ultrasound Microvascular Imaging using Deep Learning

딥러닝을 활용한 의료 초음파 미세혈관 영상화

  • Seonho Kim (Department of Information Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University) ;
  • ChunSu Park (School of Biomedical Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University) ;
  • SiYeoul Lee (Department of Information Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University) ;
  • Dongeon Lee (Department of Information Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University) ;
  • Jae-Heung Yoo (Department of Orthopedic Surgery, Busan Medical Center) ;
  • MinWoo Kim (School of Biomedical Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University)
  • 김선호 (부산대학교 정보융합공학과) ;
  • 박춘수 (부산대학교 의생명융합공학부) ;
  • 이시열 (부산대학교 정보융합공학과) ;
  • 이동언 (부산대학교 정보융합공학과) ;
  • 유재흥 (부산의료원 정형외과) ;
  • 김민우 (부산대학교 의생명융합공학부)
  • Received : 2024.10.23
  • Accepted : 2024.12.10
  • Published : 2024.12.31
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Abstract

Clinical ultrasound is a powerful diagnostic tool that enables the non-invasive detection of various diseases without the risks associated with radioactive exposure. The addition of Doppler imaging enhances its capabilities by allowing the evaluation of blood flow, which is crucial for diagnosing vascular conditions. However, the accuracy of vascular imaging is often compromised by strong clutter signals, which interfere with the detection of blood flow signals. While conventional clutter filtering techniques, such as Singular Value Decomposition (SVD), can effectively separate these signals, they are computationally intensive and may not perform well in real-time applications. Furthermore, detecting signals from microvessels is particularly challenging due to their low intensity, necessitating more advanced filtering techniques. In this study, we propose a novel clutter filtering approach based on a deep learning framework for improve vascular imaging. Furthermore, it does not rely on the use of contrast agents, making it safer and more accessible for clinical use. By overcoming the limitations of existing techniques, this framework has the potential to significantly advance the field of vascular ultrasound imaging.

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Acknowledgement

이 과제는 부산대학교 기본연구지원사업(2년)에 의하여 연구되었음.