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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
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The effect of motion correction interpolation on quantitative T1 mapping with MRI
Amitay Nachmani1, Roey Schurr2, Leo Joskowicz1
1The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Israel; The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel.
Medical Image Analysis
|December 12, 2018
Summary
Image registration artifacts in quantitative MRI (qMRI) can impact tissue property measurements. Minimizing these errors improves accuracy for in vivo brain microstructure modeling.
Area of Science:
- Medical Imaging
- Neuroimaging
- Biophysics
Background:
- Quantitative magnetic resonance imaging (qMRI) maps tissue properties using multi-contrast MR images.
- Image registration is crucial for correcting motion but can introduce interpolation artifacts.
- These artifacts may compromise the accuracy of estimated tissue properties.
Purpose of the Study:
- To quantify interpolation and resampling errors in T1-weighted images.
- To assess the impact of these errors on longitudinal relaxation time (T1) mapping.
- To investigate methods for improving in vivo brain microstructure modeling accuracy.
Main Methods:
- Simulated T1-weighted MR images were generated.
- Transformation errors from interpolation and resampling were calculated.
- The effect of these errors on T1 estimation was quantified using variable flip angles.
Main Results:
- Registration error depends on image contrast (flip angle) and spatial transformations (translation, rotation).
- Interpolation errors significantly affected T1 estimation, causing up to 10% signal error in brain gray and white matter.
- These errors directly impact the accuracy of quantitative T1 mapping.
Conclusions:
- Interpolation and resampling introduce significant errors in qMRI data.
- Minimizing registration-induced errors is essential for accurate in vivo T1 mapping.
- Improved registration techniques can enhance the reliability of brain microstructure modeling.
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