Image denoising is used extensively for MR image post-processing. The nonlocal means (NLM) filter shows excellent noise reduction while preserving detail. NLM takes advantage of the structural redundancy in MR images by comparing local neighborhoods of voxels throughout the image, and estimating the intensity of an index voxel to be denoised through a weighted average of voxel intensities. However, this excludes patches that may be similar except for rotation or reflection, and therefore does not make full use of image redundancy. We introduce a multispectral implementation of NLM incorporating rotations and reflections, finding improved performance compared to conventional non-multispectral filtering.