{"action":"created","issue":{"url":"https://api.github.com/repos/nipy/dipy/issues/677","labels_url":"https://api.github.com/repos/nipy/dipy/issues/677/labels{/name}","comments_url":"https://api.github.com/repos/nipy/dipy/issues/677/comments","events_url":"https://api.github.com/repos/nipy/dipy/issues/677/events","html_url":"https://github.com/nipy/dipy/pull/677","id":94783526,"number":677,"title":"DKI PR3 - NF: Adding standard kurtosis statistics on module dki.py ","user":{"login":"RafaelNH","id":5492638,"avatar_url":"https://avatars.githubusercontent.com/u/5492638?v=3","gravatar_id":"","url":"https://api.github.com/users/RafaelNH","html_url":"https://github.com/RafaelNH","followers_url":"https://api.github.com/users/RafaelNH/followers","following_url":"https://api.github.com/users/RafaelNH/following{/other_user}","gists_url":"https://api.github.com/users/RafaelNH/gists{/gist_id}","starred_url":"https://api.github.com/users/RafaelNH/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/RafaelNH/subscriptions","organizations_url":"https://api.github.com/users/RafaelNH/orgs","repos_url":"https://api.github.com/users/RafaelNH/repos","events_url":"https://api.github.com/users/RafaelNH/events{/privacy}","received_events_url":"https://api.github.com/users/RafaelNH/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":17,"created_at":"2015-07-13T18:38:23Z","updated_at":"2015-08-14T15:34:41Z","closed_at":null,"pull_request":{"url":"https://api.github.com/repos/nipy/dipy/pulls/677","html_url":"https://github.com/nipy/dipy/pull/677","diff_url":"https://github.com/nipy/dipy/pull/677.diff","patch_url":"https://github.com/nipy/dipy/pull/677.patch"},"body":"This works follows the work done in PR #664.\r\n\r\nNow that the functions to estimate the kurtosis tensor are implemented, we can proceed with the estimate of standard kurtosis statistics (or also known as the standard DKI rotational invariant measures). In this PR, the analytical solutions of the following statistics are implemented:\r\n\r\n- Mean kurtosis \r\n- Radial kurtosis\r\n- Axial kurtosis\r\n\r\nFor the mean kurtosis analytical solution, the implementation of the Carlson's elliptical integrals was required. Comparative to the code that Prof. Maurizio Marrale kindly supplied, I followed a more recent, general and optimized implementation of the Carlson integrals which is described here: http://arxiv.org/pdf/math/9409227v1.pdf . Despite this, estimation of the integrals are still time consuming, so I am planing to convert functions carlson_rf and carlson_rd in cython.\r\n\r\nIn alternative to the analytical solution, two numerical methods for a faster estimation of the standard DKI measures are implemented. These numerical methods are expected to be less accurate that the analytical solutions, however they can provide alternatives less computationally demanding and provide a simpler mathematical framework which will be used to further validate the analytical solutions.  \r\n"},"comment":{"url":"https://api.github.com/repos/nipy/dipy/issues/comments/131151890","html_url":"https://github.com/nipy/dipy/pull/677#issuecomment-131151890","issue_url":"https://api.github.com/repos/nipy/dipy/issues/677","id":131151890,"user":{"login":"RafaelNH","id":5492638,"avatar_url":"https://avatars.githubusercontent.com/u/5492638?v=3","gravatar_id":"","url":"https://api.github.com/users/RafaelNH","html_url":"https://github.com/RafaelNH","followers_url":"https://api.github.com/users/RafaelNH/followers","following_url":"https://api.github.com/users/RafaelNH/following{/other_user}","gists_url":"https://api.github.com/users/RafaelNH/gists{/gist_id}","starred_url":"https://api.github.com/users/RafaelNH/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/RafaelNH/subscriptions","organizations_url":"https://api.github.com/users/RafaelNH/orgs","repos_url":"https://api.github.com/users/RafaelNH/repos","events_url":"https://api.github.com/users/RafaelNH/events{/privacy}","received_events_url":"https://api.github.com/users/RafaelNH/received_events","type":"User","site_admin":false},"created_at":"2015-08-14T15:34:41Z","updated_at":"2015-08-14T15:34:41Z","body":"Hi @samuelstjean! Piesno is more stable, i.e. for all axial slices sigma is around 160 (median= 153, max=238, min=142). However, I decided to give a close look to the performances of nlmean with piesno and I detected some problems:\r\n\r\n- First, piesno only detected a small amount of background voxels.\r\n- Second, nlmean seems to work worst when the same sigma is given on all volumes. This perhaps is a consequence that nlmean was initially not proposed diffusion-weighted images.\r\n\r\nFor our record, I decided to create a new [post](http://gsoc2015dipydki.blogspot.co.uk/2015/08/rnh-post-12-attempt-to-further-improve.html) with details and figure of these issues.\r\n\r\nSince nlmean seems more compatible to the previous nose estimate I decided to keep it for the example, at least for now."}}