Quantitative evaluation of the image quality using the fast nonlocal means denoising approach in diffusion-weighted magnetic resonance imaging with high b-value

Abstract

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.