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A measure of curve fitting error for noise filtering diffusion tensor MRI data
Nikos G Papadakis1, Kay M Martin, Iain D Wilkinson
1Department of Psychology, University of Sheffield, Sheffield S10 2TP, UK. n.papadakis@shef.ac.uk
Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|August 23, 2003
Summary
A new parameter, chi2p, reliably measures diffusion tensor MRI (DT-MRI) data quality by assessing fitting errors and signal-to-noise ratio (SNR). This method effectively removes unreliable data, improving DT anisotropy maps.
Area of Science:
- Medical Imaging
- Biophysics
- Neuroimaging
Background:
- Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is crucial for neuroimaging.
- Assessing the reliability of DT-MRI data is essential for accurate analysis.
- Existing methods like the classic chi2 lack sensitivity to signal-to-noise ratio (SNR).
Purpose of the Study:
- Introduce and evaluate a novel parameter, chi2p, for assessing DT-MRI data reliability.
- Compare chi2p with the classic chi2 metric.
- Develop a method for excluding unreliable data pixels to improve DT anisotropy maps.
Main Methods:
- Investigated chi2p properties using simulations and human brain DT-MRI data.
- Compared chi2p's sensitivity to fitting error and SNR against the classic chi2.
- Developed an automated thresholding method for chi2p maps using analytical approximations.
- Applied chi2p to exclude unreliable pixels from DT anisotropy maps.
Main Results:
- Chi2p demonstrated sensitivity to both goodness-of-fit and SNR, unlike classic chi2.
- Chi2p effectively distinguished between coherent and random signal pixels.
- An objective, automated method for calculating chi2p thresholds was established.
- Exclusion of pixels with high chi2p values (low SNR, poor fits) successfully removed artifacts.
Conclusions:
- Chi2p offers a robust and practical measure of DT-MRI data reliability.
- The chi2p parameter provides a more comprehensive assessment than classic chi2.
- This method enhances the quality and interpretability of DT anisotropy maps by reducing artifacts.