Feasibility of fast non-local means noise reduction algorithm in magnetic resonance imaging using 1.5 and 3.0 T with diffusion-weighted image technique

Magnetic resonance imaging (MRI) has many advantages and has developed various pulse sequences. In particular, the diffusion weighted image (DWI) technique is widely used because it can acquire images quickly during examination of stroke, through a proper adjustment of the diffusion-weighted gradient b-value. However, a setting with inappropriate b-value causes loss of image signal that increases the influence of noise. Therefore, in this study, we quantitatively evaluated image quality after applying a variety of algorithms to the image acquired by changing the b-value and the main magnetic field in the MRI device. To acquire the image, the phantom was self-produced with an acrylic panel and chicken breast. Wiener filter, total variation (TV), and our proposed fast non-local means (FNLM) noise reduction algorithms were applied to the image. Consequently, the signal intensity at a 3.0 T magnetic field increased by a factor 4.8 compared to a 1.5 T magnetic field. Moreover, the signal-to-noise ratio and contrast-to-noise ratio were highest with the FNLM algorithm, and the values increased by factors of 9.5 and 9.9 with a 1.5 T magnetic field and by factors of 9.9 and 5.0 with a 3.0 T magnetic field compared to the noise image, respectively. The result of time resolution, the Wiener filter appeared the finest value, but had no significant difference compared to FNLM algorithm. In conclusion, our results confirmed that the proposed FNLM noise reduction algorithm can acquire both improved image quality and high processing time in MRI imaging with the DWI technique.