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Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion
Jesper L R Andersson1, Mark S Graham2, Enikő Zsoldos3
1FMRIB Centre, Oxford University, Oxford, United Kingdom.
Abstract:
Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion. Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurements. This is caused by bulk (subject or physiological) motion during the diffusion encoding part of the imaging sequence. We suggest a method to detect slices affected by signal loss and replace them by a non-parametric prediction, in order to minimise their impact on subsequent analysis. The outlier detection and replacement, as well as correction of other dMRI distortions (susceptibility-induced distortions, eddy currents (EC) and subject motion) are performed within a single framework, allowing the use of an integrated approach for distortion correction. Highly realistic simulations have been used to evaluate the method with respect to its ability to detect outliers (types 1 and 2 errors), the impact of outliers on retrospective correction of movement and distortion and the impact on estimation of commonly used diffusion tensor metrics, such as fractional anisotropy (FA) and mean diffusivity (MD). Data from a large imaging project studying older adults (the Whitehall Imaging sub-study) was used to demonstrate the utility of the method when applied to datasets with severe subject movement. The results indicate high sensitivity and specificity for detecting outliers and that their deleterious effects on FA and MD can be almost completely corrected.
Insights
This study introduces a new method to detect and fix signal loss artifacts in diffusion MRI (dMRI) caused by subject motion. The approach effectively corrects these errors, improving the accuracy of brain imaging analysis.
Area of Science:
- Neuroimaging
- Medical Physics
- Biomedical Engineering
Background:
- Diffusion MRI (dMRI) is crucial for studying brain anatomy but is prone to motion-related artifacts.
- Signal loss (dropout) in dMRI, caused by subject or physiological motion, renders measurements unusable and impacts analysis.
- Existing methods struggle to comprehensively address various dMRI distortions simultaneously.
Purpose of the Study:
- To present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) due to subject motion.
- To integrate outlier detection and replacement with other dMRI distortion corrections (susceptibility, eddy currents, motion) into a single framework.
- To evaluate the method's performance using realistic simulations and real-world data from older adults.
Main Methods:
- A non-parametric framework was developed to identify slices with signal loss.
- Detected outliers were replaced using non-parametric prediction to minimize their impact.
- The method integrated outlier correction with susceptibility-induced distortions, eddy currents, and subject motion correction.
- Performance was assessed using simulations and data from the Whitehall Imaging sub-study.
Main Results:
- The method demonstrated high sensitivity and specificity in detecting motion-induced outliers.
- Simulations confirmed the ability to correct movement and distortion artifacts.
- The framework effectively minimized the deleterious effects of outliers on diffusion tensor metrics like fractional anisotropy (FA) and mean diffusivity (MD).
Conclusions:
- The proposed framework offers an integrated approach for robust dMRI distortion correction.
- It significantly improves the reliability of dMRI data by accurately detecting and correcting signal loss.
- This method enhances the utility of dMRI for analyzing brain structure, especially in populations prone to movement artifacts.
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