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Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

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Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques.

Mark S Graham¹, Ivana Drobnjak¹, Hui Zhang¹

¹Centre for Medical Image Computing & Department of Computer Science, University College London, UK.

|November 10, 2015

Summary

This summary is machine-generated.

A new simulation framework allows direct comparison of diffusion weighted magnetic resonance (DW-MR) image correction methods. This reveals eddy_correct introduces significant errors, while eddy offers improved correction for brain microstructure analysis.

Keywords:

Artefact correction Diffusion MRI Eddy currents Motion POSSUM Simulation

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Area of Science:

Neuroimaging
Biophysics
Medical Physics

Background:

Diffusion-weighted magnetic resonance (DW-MR) imaging is crucial for estimating brain microstructure and connectivity.
Image misalignment due to artifacts like eddy-current (EC) distortion and motion degrades the quality of microstructural measures.
Existing artifact correction methods lack rigorous validation, hindering objective comparison and improvement.

Purpose of the Study:

To introduce a novel simulation framework for direct, quantitative comparison of post-processing methods for DW-MR images.
To objectively assess the performance of existing and future artifact correction techniques.
To evaluate the impact of correction quality on microstructure modeling.

Main Methods:

Developed a physics-based simulation framework to generate realistic DW-MR datasets with salient features and artifacts.
Applied the framework to quantitatively compare FSL's eddy_correct with the alternative method 'eddy'.
Investigated the impact of data acquisition parameters (e.g., number of directions, sphere coverage) on correction quality.

Main Results:

Quantitatively demonstrated that eddy_correct introduces significant errors in corrected DW-MR data.
Showcased that the 'eddy' method provides substantially improved artifact correction compared to eddy_correct.
Determined the minimum data requirements for effective correction and compared full-sphere to half-sphere acquisition protocols.

Conclusions:

The developed simulation framework enables objective, quantitative validation of DW-MR image post-processing methods.
The 'eddy' tool significantly outperforms eddy_correct, highlighting the need for updated standard correction protocols.
Correction quality directly impacts the accuracy of brain microstructure modeling, emphasizing the importance of robust artifact removal.