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An Ultrasonic Vessel-Pattern Imaging Algorithm with Low Computational Complexity

낮은 연산 복잡도를 지니는 초음파 혈관 패턴 영상 알고리즘

  • Um, Ji-Yong (Dept. of Medical IT Convergence Engineering, Kumoh National Institute of Technology)
  • Received : 2022.01.27
  • Accepted : 2022.03.22
  • Published : 2022.03.31

Abstract

This paper proposes an ultrasound vessel-pattern imaging algorithm with low computational complexity. The proposed imaging algorithm reconstructs blood-vessel patterns by only detecting blood flow, and can be applied to a real-time signal processing hardware that extracts an ultrasonic finger-vessel pattern. Unlike a blood-flow imaging mode of typical ultrasound medical imaging device, the proposed imaging algorithm only reconstructs a presence of blood flow as an image. That is, since the proposed algorithm does not use an I/Q demodulation and detects a presence of blood flow by accumulating an absolute value of the clutter-filter output, a structure of the algorithm is relatively simple. To verify a complexity of the proposed algorithm, a simulation model for finger vessel was implemented using Field-II program. Through the behavioral simulation, it was confirmed that the processing time of the proposed algorithm is around 54 times less than that of the typical color-flow mode. Considering the required main building blocks and the amount of computation, the proposed algorithm is simple to implement in hardware such as an FPGA and an ASIC.

본 논문은 낮은 연산 복잡도를 지니는 초음파 혈관 패턴 영상 알고리즘을 제안한다. 제안하는 혈관 패턴 영상 알고리즘은 혈류의 흐름 만 감지하여 혈관 패턴을 영상화하는 알고리즘이며 손가락 혈관의 패턴 영상을 추출하는 실시간 신호처리 하드웨어에 적용할 수 있다. 기존의 초음파 의료영상장비의 혈류영상 모드와 달리 제안하는 알고리즘은 혈류의 흐름 만 감지하여 영상으로 복원한다. 즉, 제안하는 영상 알고리즘은 I/Q 복조를 사용하지 않으며 클러터 필터의 출력 신호의 절대 값을 누적하는 방식으로 혈류 흐름의 유무를 검출하기 때문에, 알고리즘의 구조가 비교적 간단하다. 제안하는 영상 알고리즘의 복잡도를 검증하기 위해, Field-II 프로그램을 이용하여 손가락 혈관을 모사하는 시뮬레이션 모델을 구현하였다. 행위모사 시뮬레이션을 통해, 제안하는 알고리즘의 연산시간이 일반적인 color-flow 모드보다 약 54배 작은 것으로 확인되었다. 제안하는 영상 알고리즘에서 요구되는 주요 구성 블록과 연산량을 고려할 때, 제안하는 알고리즘은 FPGA 또는 ASIC과 같은 하드웨어에 구현되기에 용이하다.

Keywords

Acknowledgement

This research was supported by Kumoh National Institute of Technology (2021).

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