• Ung Jang and Dosik Hwang.
• School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul 120-749, Korea.
• Med Phys. 2012 Jan 1; 39 (1): 468-74.

PurposeThe aim of this study was to develop an effective postprocessing method to increase the signal-to-noise ratio in successive multi-echo magnetic resonance (MR) images acquired at multiple time points and generate high-quality multiple T(2)(*) contrast images from low-quality multi-echo images.MethodsSuccessive multi-echo MR images were acquired at multiple time points using a multigradient-recalled echo sequence at 3T and rearranged so that each pixel in the images had its own decay signal in the temporal-domain. Two different denonising approaches were implemented in the temporal-domain: (1) In a filtering approach, conventional low-pass filter, median filter, and anisotropic diffusion filter were applied to the decay signals to reduce random noise; (2) In a model-based approach, a non-negative least squares algorithm was applied for fitting to MR relaxation model for decay signals. Numerical simulations and in vivo experiments were conducted. The denoised images were compared to each other by visual inspection and analysis of mean square error (MSE) and contrast-to-noise ratio (CNR) on several regions of interest.ResultsOur proposed method suppressed noise in each multi-echo images without introducing spatial artifacts. This was a natural consequence of the proposed denoising process, which was performed in the temporal-domain and did not use any cross-pixel operation. MSEs decreased by a factor of 5.4-7.9 and CNRs increased by a factor of 5 in simulation studies. The results were consistent with the in vivo findings. Random noise in the images was effectively reduced and high-quality multiple T(2)(*) contrast images were obtained.ConclusionsThis study demonstrated that denoising methods in the temporal-domain can effectively suppress noise in the spatial domain, and increase signal-to-noise ratio (SNR) for each image of different T(2)(*) weights at multiple time points, resulting in multiple high-quality T(2)(*) contrast images.

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