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Quantitative evaluation of the image quality using the fast nonlocal means denoising approach in diffusion-weighted magnetic resonance imaging with high b -value
Journal of the Korean Physical Society ( IF 0.6 ) Pub Date : 2020-12-15 , DOI: 10.1007/s40042-020-00028-4
Jaeyoung Park , Chang-Ki Kang , Youngjin Lee

Brain images acquired using the diffusion-weighted imaging (DWI) method indicate that the diagnostic efficiency of infarction improves and the noise increases as the b-value increases. In this study, we designed a fast nonlocal means (FNLM) noise reduction algorithm and evaluated its effectiveness for de-noising brain images with high b-values. The designed algorithm uses an approach that measures the similarity of local parts in an image, calculates weights based on the result, and uses the principle of reducing processing time using a simplification of the calculation. To demonstrate the effectiveness of the algorithm, we compared the qualities of the images obtained using FNLM with those obtained using previously developed algorithms with noise reduction performance and no-reference image-quality assessment parameters. The results of applying the FNLM noise reduction algorithm to DWI images obtained at high b-values indicated superior quantitative characteristics. In particular, the signal-to-noise ratio, coefficient of variation, and blind/referenceless image spatial quality evaluator (BRISQUE) results using the proposed FNLM algorithm were approximately 1.84, 1.44, and 1.21 times better than those of the noisy image, respectively. In conclusion, our results verified that the FNLM approach achieves higher noise reduction efficiency in diffusion-weighted magnetic resonance imaging.



中文翻译:

在高b值扩散加权磁共振成像中使用快速非局部均值去噪方法对图像质量进行定量评估

使用扩散加权成像(DWI)方法获取的脑部图像表明,随着b值的增加,梗塞的诊断效率提高并且噪声也增加。在这项研究中,我们设计了一种快速的非局部均值(FNLM)降噪算法,并评估了其对高b值。设计的算法使用一种方法来测量图像中局部部分的相似性,根据结果计算权重,并使用简化计算来减少处理时间的原理。为了证明该算法的有效性,我们将使用FNLM获得的图像质量与使用先前开发的具有降噪性能和无参考图像质量评估参数的算法获得的图像质量进行了比较。将FNLM降噪算法应用于以高b值获得的DWI图像的结果表明,该方法具有出色的定量特性。特别是,使用提出的FNLM算法,信噪比,变异系数和盲/无参考图像空间质量评估器(BRISQUE)结果约为1.84、1.44和1。分别是嘈杂图像的21倍。总之,我们的结果验证了FNLM方法在扩散加权磁共振成像中实现了更高的降噪效率。

更新日期:2020-12-15
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