Earticle

초록

영어

Background: This study systematically compared the performances of fast non-local means (FNLM), conventional non-local means (NLM), and adaptive non-local means (ANLM) algorithms for Rician noise reduction in clinical breast magnetic resonance imaging (MRI). Materials and Methods: Rician noise with standard deviations of 0.05, 0.10, and 0.15 was synthetically introduced into pre-contrast T1-weighted breast MRI images obtained from 50 patients in a publicly available clinical dataset. For each noise level, the FNLM search window size was optimized using a root mean square error (RMSE)-based tuning procedure. The optimized FNLM was then quantitatively compared with NLM and ANLM. Image quality was assessed using RMSE, structural similarity index (SSIM), high-frequency error norm (HFEN), gradient magnitude similarity deviation, and edge preservation index (EPI). Computational efficiency was evaluated in a MATLAB (MathWorks) environment using central processing unit-based processing. Results and Discussion: The relative performance of FNLM varied according to noise level and evaluation metric. Compared with ANLM, FNLM achieved lower RMSE and HFEN and higher SSIM and EPI across most noise levels, while showing comparable or improved performance relative to NLM. Linear mixed-effects analysis confirmed significant algorithmic differences depending on noise severity. Regarding computational efficiency, FNLM was approximately 7.8–25.1 times faster than ANLM and 2.7–3.2 times faster than NLM across noise levels. Conclusion: The optimized FNLM algorithm provides competitive denoising performance while substantially improving computational efficiency in clinical breast MRI with Rician noise.

목차

ABSTRACT
Introduction
Materials and Methods
1. Acquisition of Breast MRI Images and Addition of Rician Noise
2. Modeling and Implementation of the NLM and FNLM Algorithm
3. Modeling and Implementation of the ANLM Algorithm
4. FNLM Search Window Optimization
5. Quantitative Evaluation of Denoising Performance and Time Resolution
6. Statistical Analysis
Results
1. Optimization of FNLM Search Window Size Based on RMSE
2. Quantitative Comparison of Denoising Performance across Algorithms
3. Comparisons of Computational Time between the FNLM and ANLM Algorithms
Discussion
Conclusion
Article Information
References

키워드

저자

• Junho Jeong [ Department of Radiological Science, Gachon University, Incheon, Republic of Korea ]
• Gayoung Kim [ Department of Radiological Science, Gachon University, Incheon, Republic of Korea ]
• Seoyeon Park [ Department of Radiological Science, Gachon University, Incheon, Republic of Korea ]
• Sewon Lim [ Department of Health Science, General Graduate School of Gachon University, Incheon, Republic of Korea ]
• Youngjin Lee [ Department of Radiological Science, Gachon University, Incheon, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  방사선방어학회지 [Journal of Radiation Protection and Research]

• 간기

  계간

• pISSN

  2508-1888

• 수록기간

  1976~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 559 DDC 629