Use of fast non-local means approach for noise reduction in diffusion weighted magnetic resonance imaging with high b-value

다국어 초록 (Multilingual Abstract)

In this study, we designed a fast non-local means (FNLM) noise reduction approach and evaluated its effectiveness for denoising brain images with high b-values. This study was performed at 3 T MRI (Verio, Siemens, Germany). Diffusion-weighted image (DWI) with a spin-echo echo planar imaging pulse sequence, which is the fastest imaging method currently available, was used to produce the images. In the process of denoising, the NLM algorithm has the advantage of minimizing blurring and artifacts after image processing, as it is calculated in reference to the surrounding pixels in the region of interest (ROI). However, the process of calculating the weight is difficult to apply in a clinical setting due to the length of time required. Therefore, this study has changed the process of calculating the weight from 2D to 1D. To demonstrate the effectiveness of the algorithm, we compared the qualities of the images obtained using FNLM with those obtained using previously developed algorithms using noise reduction performance and no-reference image quality assessment parameters. A visual inspection of the images indicates that our proposed algorithm achieved better denoising efficiency compared with conventional methods. In particular, upon observing the enlarged image, we confirmed that the noise was reliably reduced, as shown by the red arrow, by applying the proposed algorithm. In particular, the results of applying the FNLM noise reduction algorithm to DWI images obtained at high b-values indicated superior quantitative characteristics. In this study, we investigated and analyzed the proposed FNLM noise reduction algorithm in high b-value images with DWI.