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Published on: July 24, 2017
Estimation of extracellular volume from regularized multi-shell diffusion MRI
Ofer Pasternak1, Martha E Shenton, Carl-Fredrik Westin
1Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Abstract:
Diffusion MRI measures micron scale displacement of water molecules, providing unique insight into microstructural tissue architecture. However, current practical image resolution is in the millimeter scale, and thus diffusivities from many tissue compartments are averaged in each voxel, reducing the sensitivity and specificity of the measurement to subtle pathologies. Recent studies have pointed out that eliminating the contribution of extracellular water increases the sensitivity of the diffusion measures to tissue architecture. Moreover, in brain imaging, estimation of the extracellular volume appears to indicate pathological processes such as atrophy, edema and neuroinflammation. Here we study the free-water method, which assumes a bi-tensor model. We add low b-value shells to a regular DTI acquisition and present methods to improve the estimation of the model parameters using the extra information. In addition, we define a Laplace-Beltrami regularization operator that further stabilizes the multi-shell estimation.
Insights
This study enhances diffusion MRI by refining the free-water method to better detect subtle brain pathologies. Improved estimation techniques increase sensitivity to microstructural tissue architecture changes.
Area of Science:
- Neuroimaging
- Biomedical Engineering
- Diffusion MRI Physics
Background:
- Diffusion MRI offers insights into tissue microstructure but is limited by low resolution, averaging signals from multiple compartments.
- Eliminating extracellular water signal improves diffusion measures' sensitivity to subtle pathologies.
- Extracellular volume estimation in brain imaging can reveal conditions like atrophy, edema, and neuroinflammation.
Purpose of the Study:
- To enhance the free-water method for improved diffusion MRI analysis.
- To increase sensitivity and specificity of diffusion measures for detecting microstructural tissue changes.
- To stabilize multi-shell diffusion imaging parameter estimation.
Main Methods:
- Utilized a bi-tensor model within the free-water method.
- Incorporated low b-value shells into standard Diffusion Tensor Imaging (DTI) acquisition.
- Developed methods to improve model parameter estimation using additional data.
- Introduced a Laplace-Beltrami regularization operator for enhanced multi-shell estimation stability.
Main Results:
- Demonstrated improved estimation of free-water model parameters.
- Showcased enhanced sensitivity of diffusion measures to tissue architecture.
- Validated the stabilization effect of Laplace-Beltrami regularization on multi-shell data.
Conclusions:
- The refined free-water method with multi-shell data and regularization offers a more sensitive approach to diffusion MRI.
- This technique holds promise for earlier and more accurate detection of neuropathologies.
- Improved microstructural analysis can advance the understanding and diagnosis of brain diseases.
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