# Tensor2metric works not well to give negative RD, AD and RD inside the brain

**URL:** <https://community.mrtrix.org/t/tensor2metric-works-not-well-to-give-negative-rd-ad-and-rd-inside-the-brain/1333>\
**Category:** Uncategorized\
**Tags:** error, preprocessing\
**Created:** [November 27, 2017, 10:01pm UTC](https://community.mrtrix.org/t/tensor2metric-works-not-well-to-give-negative-rd-ad-and-rd-inside-the-brain/1333 "2017-11-27T22:01:50Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![ThijsDhollander](https://community.mrtrix.org/user_avatar/community.mrtrix.org/thijsdhollander/32/1617_2.png) [@ThijsDhollander](https://community.mrtrix.org/u/ThijsDhollander)\
**Post date:** [January 29, 2018, 4:00am UTC](https://community.mrtrix.org/t/tensor2metric-works-not-well-to-give-negative-rd-ad-and-rd-inside-the-brain/1333/2 "2018-01-29T04:00:39Z")

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Hi @Junhao_Wen,

This is not so surprising, as the `dwi2tensor` implementation isn’t constrained to produce tensors with positive eigenvalues. Once one or more eigenvalues are negative, the FA, MD, RD and AD metrics can go beyond their intended physically sensible range, e.g. MR, RD and AD values below 0 or FA values beyond 1. The most straightforward (yet naive) way to deal with this, if these values cause you trouble, is to cap them to a sensible range; i.e. make the negative values zero.

Interpolation will indeed “spread” the problem slightly (potentially). Depending on the type of interpolation, even an image with only positive values (though some zeros) may _introduce_ negative values. To avoid this, stick to linear interpolation (not cubic and beyond).

Cheers,  
Thijs

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