Purpose: To develop an effective method that can suppress noise in successive multiecho T2(*)-weighted magnetic resonance (MR) brain images while preventing filtering artifacts. Materials and Methods: For the simulation experiments, we used multiple T2-weighted images of an anatomical brain phantom. For in vivo experiments, successive multiecho MR brain images were acquired from five healthy subjects using a multiecho gradient-recalled-echo (MGRE) sequence with a 3T MRI system. Our denoising method is a nonlinear filter whose filtering weights are determined by tissue characteristics among pixels. The similarity of the tissue characteristics is measured based on the l2-difference between two temporal decay signals. Both numerical and subjective evaluations were performed in order to compare the effectiveness of our denoising method with those of conventional filters, including Gaussian low-pass filter (LPF), anisotropic diffusion filter (ADF), and bilateral filter. Root-mean-square error (RMSE), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were used in the numerical evaluation. Five observers, including one radiologist, assessed the image quality and rated subjective scores in the subjective evaluation. Results: Our denoising method significantly improves RMSE, SNR, and CNR of numerical phantom images, and CNR of in vivo brain images in comparison with conventional filters (P < 0.005). It also receives the highest scores for structure conspicuity (8.2 to 9.4 out of 10) and naturalness (9.2 to 9.8 out of 10) among the conventional filters in the subjective evaluation. Conclusion: This study demonstrates that high-SNR multiple T2(*)-contrast MR images can be obtained using our denoising method based on tissue characteristics without noticeable artifacts. Evidence level: 2. J. MAGN. RESON. IMAGING 2017;45:1835–1845.
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging