The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

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Field Map Estimation for Effective Fat Quantification at High Field MRI

고자장 자기공명영상에서 효율적인 지방 정량화를 위한 필드 맵 측정 기술

  • 은성종 (가천대학교 전자계산학과) ;
  • 황보택근 (가천대학교 IT대학 인터랙티브미디어학과)
  • Received : 2014.10.07
  • Accepted : 2014.11.18
  • Published : 2014.11.28
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Abstract

The number of fatty liver patients is sharply growing due to the rapid increase in the incidence of metabolic syndrome, which can lead to diseases such as abdominal obesity, hypertension, diabetes, and hyperlipidemia. Early diagnosis requires examinations using magnetic resonance imaging (MRI), wherein quantitative analyses are implemented through a professional water-fat separation method in many cases, as the intensity values of the areas of interest and non-interest are considerably similar or the same. However, such separation method generates inaccurate results in high magnetic fields, where the inhomogeneity of the fields increases. To overcome the limits of such conventional fat quantification methods, this paper proposes a field map estimation method that is effective in high magnetic fields. This method generates field maps through echo images that are obtained using the existing IDEAL sequences, and considers the wrapping degree of the field maps. Then clustering is performed to separate calibration areas, the least square fits based on the region growing method schema of the separated calibration areas, and the histograms are adjusted to separate the water from the fats. In experiment results, our proposed method had a superior fat detection rate of an average of 86.4%, compared to the ideal method with an average of 61.5% and Yu's method with an average of 62.6%. In addition, it was confirmed that the proposed method had a more accurate water detection rate of 98.4% on the average than the 88.6% average of the fat saturation method.

최근 복부비만, 고혈압, 당뇨, 고지혈증과 같은 대사성 증후군에 속하는 질환이 급격히 증가하여 지방간 환자수가 급격히 증가하고 있다. 이를 위해 자기공명영상을 통한 검진이 요구되는데 관심 영역과 관심 영역이 아닌 영역의 intensity 값이 상당히 유사하거나 동일한 경우가 많기 때문에 전문적인 물, 지방 분리 방법을 통해서 분석하는 경우가 많다. 그러나 이러한 분리법은 필드의 불균일성이 큰 고자장으로 갈수록 부정확한 결과가 도출하게 된다. 본 논문은 이러한 기존 분리법의 한계를 극복하고자, 고자장에서도 적용이 가능한 필드 맵 측정 방법을 제안한다. 제안 방법은 기존 IDEAL 시퀀스로 얻어진 에코 영상을 통해 필드 맵을 생성하고, 클러스터링 작업, 그리고 영역성장법 스키마 기반의 least square fit을 통한 필드 맵의 보정, 마지막으로 필드 맵의 homogeneity를 위한 경계선의 보정을 수행하여 최종적으로 물과 지방을 분리한다. 실험결과 IDEAL방법이 평균 61.5%, Yu의 방법이 평균 62.6%, 제안 방법이 평균 86.4%의 지방 검출율로 제안방법이 월등함을 확인하였다. 또한 물 검출율 역시 제안 방법이 평균 98.4%의 물 검출율로 Fat saturation의 평균 88.6% 보다 높은 정확도를 확인 할 수 있었다.

Keywords

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