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When left uncorrected, dropout will be erroneously interpreted as high diffusivity in the affected direction. We present a method to automatically detect dropout, and to replace the affected measurements with imputed values.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Signal dropout is detected by deriving an outlier score from a simple harmonic oscillator\u2010based reconstruction and estimation (SHORE) fit of all measurements. The outlier score is defined to detect measurements that are substantially lower than predicted by SHORE in a relative sense, while being less sensitive to measurement noise in cases of weak baseline signal. A second SHORE fit is based on detected inliers only, and its predictions are used to replace outliers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our method is shown to reliably detect and accurately impute dropout in simulated data, and to achieve plausible results in corrupted in vivo dMRI measurements. Computational effort is much lower than with previously proposed alternatives.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Deriving a suitable outlier score from SHORE results in a fast and accurate method for detection and imputation of dropout in diffusion MRI. It requires measurements with multiple b values (such as multi\u2010shell or DSI), but is independent from the models used for analysis (such as DKI, NODDI, deconvolution, etc.).<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.27893","type":"journal-article","created":{"date-parts":[[2019,7,5]],"date-time":"2019-07-05T06:33:46Z","timestamp":1562308426000},"page":"2286-2298","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["SHORE\u2010based detection and imputation of dropout in diffusion MRI"],"prefix":"10.1002","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9795-0541","authenticated-orcid":false,"given":"Alexandra","family":"Koch","sequence":"first","affiliation":[{"name":"German Center for Neurodegenerative Diseases (DZNE) Bonn Germany"},{"name":"Department of Computer Science University of Bonn Bonn Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrei","family":"Zhukov","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Bonn Bonn Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8946-9141","authenticated-orcid":false,"given":"Tony","family":"St\u00f6cker","sequence":"additional","affiliation":[{"name":"German Center for Neurodegenerative Diseases (DZNE) Bonn Germany"},{"name":"Department of Physics and Astronomy University of Bonn Bonn Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2706-7163","authenticated-orcid":false,"given":"Samuel","family":"Groeschel","sequence":"additional","affiliation":[{"name":"Department of Child Neurology University Children's Hospital T\u00fcbingen T\u00fcbingen Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1200-7248","authenticated-orcid":false,"given":"Thomas","family":"Schultz","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Bonn Bonn Germany"},{"name":"Bonn\u2010Aachen International Center for Information Technology Bonn Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,7,4]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1053-8119(03)00336-7"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.10.019"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910320116"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.21132"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.06.058"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.20426"},{"key":"e_1_2_7_8_1","doi-asserted-by":"crossref","unstructured":"ScherrerB WarfieldSK.Retrospective local artefacts detection in diffusion\u2010weighted images using the Random Sample Consensus (RANSAC) paradigm. 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