Ms. Johanna Dubos Profile

Johanna Dubos

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Proceedings Article | 3 April 2023 Poster + Paper

Dmriprep: open-source diffusion MRI quality control framework with graphical user interface

Johanna Dubos, Sang Kyoon Park, Roza Vlasova, Juan Carlos Prieto, Martin Styner

Proceedings Volume 12464, 124643A (2023) https://doi.org/10.1117/12.2654470

KEYWORDS: Quality control, Human-machine interfaces, Diffusion weighted imaging, Diffusion magnetic resonance imaging, Visualization, Neuroimaging, Diffusion, Brain, Magnetic resonance imaging, Diffusion tensor imaging

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In the last decade, investigating white matter microstructure and connectivity via diffusion MRI (dmri) has become a crucial cornerstone in neuroimaging studies. However, even modern dmri sequences have inherently a low signal-to-noise ratio and long acquisition times, depending on the spatial resolution. Furthermore, many types of artifacts complicate the appropriate analysis of dmri, necessitating appropriate quality control (QC) procedures, including exclusion and/or correction of inappropriate/erroneous dmri data. Our group has been developing and promoting QC procedures and tools to the community to enable appropriate dmri analyses. Since its development in 2011, our DTIPrep QC tool has become a major tool due its ease of use and dmri QC performance. Over the years, novel developments in acquisition and artifact correction methods have led to a need to modernize DTIPrep. Here, we present a novel diffusion MRI analysis environment called dtiplayground with a fully redesigned and significantly enhanced QC module dmriprep, and its graphical user interface dmriprep-ui, building on in-house developed code, FSL and dipy. The user interface is designed to be a unified, user friendly tool for thorough QC of dMRI data.Artifacts addressed by dmriprep include eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, slice-wise and gradient-wise intensity inconsistencies, and susceptibility artifacts. It further provides an user interface for visual QC of gradients and automated tractography. In summary, our work presents a novel open-source framework for modern comprehensive dmri QC.

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