Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Dynamic Parameter Visualization and Noise Suppression Techniques for Contrast-Enhanced Ultrasonography

조영증강 초음파진단을 위한 동적 파라미터 가시화기법 및 노이즈 개선기법

  • 김호준 (한동대학교 전산전자공학부)
  • Received : 2015.04.08
  • Accepted : 2015.05.12
  • Published : 2015.07.15
⟨ Previous Next ⟩

Abstract

This paper presents a parameter visualization technique to overcome the limitation of the naked eye in contrast-enhanced ultrasonography. A method is also proposed to compensate for the distortion and noise in ultrasound image sequences. Meaningful parameters for diagnosing liver disease can be extracted from the dynamic patterns of the contrast enhancement in ultrasound images. The visualization technique can provide more accurate information by generating a parametric image from the dynamic data. Respiratory motions and noise from micro-bubble in ultrasound data may cause a degradation of the reliability of the diagnostic parameters. A multi-stage algorithm for respiratory motion tracking and an image enhancement technique based on the Markov Random Field are proposed. The usefulness of the proposed methods is empirically discussed through experiments by using a set of clinical data.

본 논문에서는 조영증강 초음파영상의 분석과정에서 육안판별의 한계를 극복하기 위한 파라미터 가시화기법을 소개하고, 이 과정에서 영상의 왜곡과 노이즈를 보정하기 위한 방법론을 제시한다. 초음파영상에서 조영제의 전이형태에 대한 동적패턴은 간질환 진단에서 의미있는 파라미터가 되는데, 전이시간 정보와 조영증강 패턴을 정적인 단일영상으로 표현함으로써 급속도로 진행되는 동영상에서 정확한 정보를 효과적으로 판별할 수 있게 한다. 진단파라미터 데이터의 신뢰도를 저하시키는 요인으로 호흡에 의한 흔들림현상과 마이크로 버블에 의한 노이즈를 들 수 있다. 이에 대한 대안으로 영상의 움직임추적을 위한 다단계 알고리즘과 마르코프 랜덤 필드 모델에 기반한 영상개선기법을 제안한다. 실제 임상데이터를 사용한 실험결과를 통하여, 제안된 방법의 유용성을 실험적으로 고찰한다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. V. Salvatore, A. Borghi, E. Sagrini, M. Galassi, A. Gianstefani, L. Bolondi, F. Piscaglia, "Quantification of Enhancement of Focal Liver Lesions During Contrast-Enhanced Ultrasound(CEUS). Analysis of Ten Selected Frames is More Simple But As Reliable As the Analysis of the Entire Loop for Most Parameters," European Journal of Radiology, Vol. 81, pp. 709-713, 2012. https://doi.org/10.1016/j.ejrad.2011.01.097
  2. A. D. Marchi, E. B. del Prever, F. Cavallo, S. Pozza, A. Linari, P. Lombardo, A. Comandone, R. Piana, C. Faletti, "Perfusion Pattern and Time of Vascularisation with CEUS Increase Accuracy in Differentiating Between Benign and Malignant Tumours in 216 Musculoskeletal Soft Tissue Masses," European Journal of Radiology, Vol. 84, pp. 142-150, 2015. https://doi.org/10.1016/j.ejrad.2014.10.002
  3. Margot Braning, Peter N. Burns, Stephanie R. Wilson, "Blood Flow Patterns in Focal Liver Lesions at Microbubble-enhanced US," Radio Graphics, Vol. 24, No. 4, pp. 921-935, 2004.
  4. Na Li, Hong Ding, Peili Fan, Xiuan Lin, Chen Xu, Wenping Wang, Zhizhang Xu, and Jiyao Wang, "Intrahepatic Transit Time Predicts Liver Fibrosis in patients with Chronic Hepatitis B : Quantitative Assessment with Contrast-Enhanced Ultrasonography," Ultrasound in Med. & Biol., Vol. 36, No. 7, pp. 1066-1075. 2010. https://doi.org/10.1016/j.ultrasmedbio.2010.04.012
  5. A. Lim, S. Taylor-Robinson, N. Patel, R. Eckersley, R. Goldin, G. Hamilton, G. Foster, H. Thomas, D. Cosgrove, M. Blomley, "Hepatic Vein Transit Time Using a Microbubble Agent Can Predict Disease Severity Non-invasively in Patients with Hepatitis C," Liver, Vol. 54, pp. 128-133, 2005.
  6. N. Rognin, M. Arditi, L. Mercier, P. Frinking, M. Schneider, G. Perrenoud, A. Anaye, J. Meuwly, and F. Tranquart, "Parametric Imaging for Characterizing Focal Liver Lesions in Contrast-Enhanced Ultrasound," IEEE Transaction on Ultrasonics, Ferroelectrics, and Frequency Control., Vol. 57, No. 11, pp. 2503-2511, 2010. https://doi.org/10.1109/TUFFC.2010.1716
  7. R. Grimm, S. Furst, M. Souvatzoglou, C. Forman, J. Hutter, I. Dregely, S. Ziegler, B. Kiefer, J. Hornegger, K. Block, S. Nekolla, "Self-Gated MRI Motion Modeling for Respiratory Motion Compensation in Integrated PET/MRI," Medical Image Analysis, Vol. 19, pp. 110-120, 2015. https://doi.org/10.1016/j.media.2014.08.003
  8. S. Mule, N. Kachenoura, O. Lucidarme, A. D. Oliverira, C. pellot-Barakat, A. Herment, and F. Frouin, "An Automatic Respiratory Gating Method for the Improvement of Microcirculation Evaluation: Application to Contrast-enhanced Ultrasound Studies of Focal Liver Lesions," Physics in Medicine and Biology, Vol. 56, pp. 5153-5165, 2011. https://doi.org/10.1088/0031-9155/56/16/005
  9. S. Yousefi, N. Kehtarnavaz, Y. Cao, A.R. Razlighi, "Bilateral Markov Mesh Random Field and Its Application to Image Restoration," Journal of Visual Communication and Image Representaton, Vol. 23, pp. 1051.1059, 2012. https://doi.org/10.1016/j.jvcir.2012.06.001
  10. Kaizhi Wu, Xi Chen, Mingyue Ding, "Deep Learning Based Classification on Focal Liver Lesions with Contrast-Enhanced Ultrasound," Optik, Vol. 125, pp. 4057-4063, 2014. https://doi.org/10.1016/j.ijleo.2014.01.114