Based　on　the　Radon　transform,　a　wavelet　multiscale　denoising　method　is　proposed　for　MR　images.　The　approach　explicitly　accounts　for　the　Rician　nature　of　MR　data.　Based　on　noise　statistics　we　apply　the　Radon　transform　to　the　original　MR　images　and　use　the　Gaussian　noise　model　to　process　the　MR　sinogram　image.　A　translation　invariant　wavelet　transform　is　employed　to　decompose　the　MR　＇sinogram＇　into　multiscales　in　order　to　effectively　denoise　the　images.　Based　on　the　nature　of　Rician　noise　we　estimate　noise　variance　in　different　scales.　For　the　final　denoised　sinogram　we　apply　the　inverse　Radon　transform　in　order　to　reconstruct　the　original　MR　images.　Phantom,　simulation　brain　MR　images,　and　human　brain　MR　images　were　used　to　validate　our　method.　The　experiment　results　show　the　superiority　of　the　proposed　scheme　over　the　traditional　methods.　Our　method　can　reduce　Rician　noise　while　preserving　the　key　image　details　and　features.　The　wavelet　denoising　method　can　have　wide　applications　in　MRI　as　well　as　other　imaging　modalities.