노이즈 레벨 및 유사도 평가 기반 저선량 조건의 전산화 단층 검사 영상에서의 비지역적 평균 알고리즘의 최적화

• 영문명: Optimization of Non-Local Means Algorithm in Low-Dose Computed Tomographic Image Based on Noise Level and Similarity Evaluations
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 정하선(Ha-Seon Jeong) 김이준(Ie-Jun Kim) 박수빈(Su-Bin Park) 박수연(Suyeon Park) 오윤지(Yunji Oh) 이우석(Woo-Seok Lee) 서강현(Kang-Hyeon Seo) 이영진(Youngjin Lee)
• 간행물 정보: 『방사선기술과학』제47권 제1호, 39~48쪽, 전체 10쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2024.02.29

영문 초록

In this study, we optimized the FNLM algorithm through a simulation study and applied it to a phantom scanned by low-dose CT to evaluate whether the FNLM algorithm can be used to obtain improved image quality images. We optimized the FNLM algorithm with MASH phantom and FASH phantom, which the algorithm was applied with MATLAB, increasing the smoothing factor from 0.01 to 0.05 with increments of 0.001 and measuring COV, RMSE, and PSNR values of the phantoms. For both phantom, COV and RMSE decreased, and PSNR increased as the smoothing factor increased. Based on the above results, we optimized a smoothing factor value of 0.043 for the FNLM algorithm. Then we applied the optimized FNLM algorithm to low dose lung CT and lung CT under normal conditions. In both images, the COV decreased by 55.33 times and 5.08 times respectively, and we confirmed that the quality of the image of low dose CT applying the optimized FNLM algorithm was 5.08 times better than the image of lung CT under normal conditions. In conclusion, we found that the smoothing factor of 0.043 among the factors of the FNLM algorithm showed the best results and validated the performance by reducing the noise in the low-quality CT images due to low dose with the optimized FNLM algorithm.

목차

Ⅰ. 서 론

Ⅱ. 재료 및 방법

Ⅲ. 결 과

Ⅳ. 고 찰

Ⅴ. 결 론

REFERENCES

키워드

방사선 전산화 단층 촬영 선량 감소 FNLM 알고리즘 폐 영상 Radiation Computed Tomography Dose Reduction FNLM Algorithm Lung Image