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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data
Chantal M W Tax1, Ben Jeurissen2, Sjoerd B Vos1
1Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
Neuroimage
|August 10, 2013
Summary
This study introduces a new method for calibrating the response function in diffusion-weighted MRI, improving the accuracy of fiber orientation estimation in white matter. The automated approach avoids manual settings, enhancing diffusion MRI analysis.
Area of Science:
- Neuroimaging
- Biomedical Engineering
- Diffusion MRI
Background:
- Current diffusion-weighted MRI resolutions often result in voxels containing multiple, differently oriented white matter fiber populations ('crossing fibers').
- Spherical deconvolution techniques can analyze this intra-voxel signal heterogeneity, balancing hardware/acquisition constraints with reliable fiber orientation distribution function (fODF) reconstruction.
- Inaccurate response function (RF) calibration in spherical deconvolution can lead to spurious fODF peaks, negatively impacting tractography results.
Purpose of the Study:
- To develop a robust and automated method for response function (RF) calibration in diffusion-weighted MRI.
- To overcome limitations of current RF calibration methods that rely on user-defined settings and the diffusion tensor model.
- To improve the reliability of fiber orientation distribution function (fODF) estimation and subsequent tractography, especially in complex white matter regions.
Main Methods:
- A novel recursive framework was developed to exclude 'crossing fibers' voxels for RF calibration.
- The proposed method circumvents the need for user-defined settings and fractional anisotropy (FA) thresholds.
- The approach was evaluated using simulations and applied to both in vivo and ex vivo diffusion MRI datasets.
Main Results:
- The developed method enables robust and automated RF calibration without requiring predefined FA thresholds.
- The approach successfully calibrated the RF in datasets where a priori settings are challenging.
- Results demonstrate improved reliability in fODF estimation and tractography by accurately calibrating the RF.
Conclusions:
- The proposed automated RF calibration method offers a significant advancement for spherical deconvolution techniques in diffusion MRI.
- This framework enhances the applicability of diffusion MRI tractography in complex white matter structures.
- The method provides a reliable and user-independent approach to a critical step in diffusion MRI analysis.
