Abstract
In this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them. Numerous methods and software packages exist to correct these artefacts, some of which have become de-facto standards, but none have been subject to rigorous validation. The ultimate aim of these techniques is improved image alignment, yet in the literature this is assessed using either qualitative visual measures or quantitative surrogate metrics. Here we introduce a simulation framework that allows for the direct, quantitative assessment of techniques, enabling objective comparisons of existing and future methods. DW-MR datasets are generated using a process that is based on the physics of MRI acquisition, which allows for the salient features of the images and their artefacts to be reproduced. We demonstrate the application of this framework by testing one of the most commonly used methods for EC correction, registration of DWIs to b = 0, and reveal the systematic bias this introduces into corrected datasets.
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Acknowledgements
MG is supported by the EPSRC (EP/L504889/1). MG and HZ are supported by the Royal Society International Exchange Scheme with China. HZ is additionally supported by the EPSRC (EP/L022680/1) and the MRC (MR/L011530/1). ID is supported by the Leverhulme Trust.
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Graham, M.S., Drobnjak, I., Zhang, H. (2015). A Simulation Framework for Quantitative Validation of Artefact Correction in Diffusion MRI. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_50
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DOI: https://doi.org/10.1007/978-3-319-19992-4_50
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