In order to improve the quality of Magnetic Resonance (MR) images, a denoising algorithm for a MR image using Dual-Tree Contourlet (DT-Contourlet) transform is proposed.The distribution model of noise of the MR image is investigated, and a method to estimate the noise parameters of the squared magnitude MR image is derived based on the assumption that such noise obeys Rician distribution.Then, the pyramidal dual-tree directional filter bank of DT-Contourlet is analyzed to show that DT-Contourlet maintains the flexibility direction selectivity of the Contourlet transform, and overcomes the shortcomes of the Contourlet in lack of shift invariance.After that, the locally adaptive window is used to compute the shrinkage factor to shrink the DT-Contourlet coefficients of the squared magnitude MR image in the DT-Contourlet domain by calculating the Variance Homogeneity Measurement (VHM).Finally, the denoising algorithm to MR image is implemented via the inverse DT-Contourlet transform.Experimental results show that the Peak Signal-Noise Ratio (PSNR) of simulated MR images by proposed algorithm is superior to that by traditional algorithms.With different noise variances, the PSNR of new algorithm is high 2.13 dB and 0.91 dB than those of wavelet-based and contourlet-based algorithms averagely.For visual quality, the proposed algorithm can reduce the noise in MR images effectively and retain more details simultaneously.