Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Same Brain, Different Look? – the Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

Version 1 : Received: 25 June 2021 / Approved: 6 July 2021 / Online: 6 July 2021 (11:29:06 CEST)
Version 2 : Received: 1 September 2021 / Approved: 6 September 2021 / Online: 6 September 2021 (13:20:18 CEST)

A peer-reviewed article of this Preprint also exists.

Journal reference: Journal of Clinical Medicine 2021
DOI: 10.3390/jcm10214987


In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from e.g. diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19-54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps,obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

Supplementary and Associated Material Data (such as FA and FD values, ROIs, scanning protocols and data for age correlation) are stored at the Open Science Framework and openly available


Diffusion Magnetic Resonance Imaging; White Matter; Fractional anisotropy; Multi-centre; Reproducibility; Imaging artefacts; Ageing



Comments (1)

Comment 1
Received: 6 September 2021
Commenter: Ronja Thieleking
Commenter's Conflict of Interests: Author
Comment: The manuscript was reviewed by 2 Reviewers and thanks to the Reviewers’ comments, we were able to improve the manuscript extensively. Changes in the manuscript are marked with the “Track changes”-function. In order to make the revision process as transparent as possible, I upload the responses to the Reviewers together with the manuscript as submission files.
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