# Dwidenoise: which extent is better for me?

**URL:** <https://community.mrtrix.org/t/dwidenoise-which-extent-is-better-for-me/1913>\
**Category:** Uncategorized\
**Tags:** preprocessing\
**Created:** [September 17, 2018, 1:04pm UTC](https://community.mrtrix.org/t/dwidenoise-which-extent-is-better-for-me/1913 "2018-09-17T13:04:40Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![maxpietsch](https://community.mrtrix.org/user_avatar/community.mrtrix.org/maxpietsch/32/25_2.png) [@maxpietsch](https://community.mrtrix.org/u/maxpietsch)\
**Post date:** [September 19, 2018, 8:30am UTC](https://community.mrtrix.org/t/dwidenoise-which-extent-is-better-for-me/1913/6 "2018-09-19T08:30:37Z")

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1. The assumption of the method used in dwidenoise is that in a local patch the noiseless signal is sparse and can be represented using a low dimensional projection of the measured signal. Noise on the other hand is not sparse. I think, for this decomposition, the actual size of the voxels should matter much less than the extent in voxels.
2. That depends on whether the images share the same noise characteristics. Did you see this related question? [Use of Prescan Normalize (Siemens) and dwidenoise](https://community.mrtrix.org/t/use-of-prescan-normalize-siemens-and-dwidenoise/1666/3)

If you go for concatenation and splitting, you can use `mrconvert`'s `-set_property` and `-get_property` to manually set the transformations back to their original values.

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