Diffusion-weighted images (DWI) and Diffusion Tensor Imaging (DTI) are well-known and powerful imaging techniques in MRI. For DTI images, the most used measurements are the fractional anisotropy (FA) and apparent diffusion coefficient (ADC). However, the limitations of FA and ADC formalism are also vastly debated due to low tissue contrast for ADC maps and measurement artifacts present in crossing-fiber orientation for FA maps. Although the DTI evaluation has evolved continually in recent years, there are still struggles regarding the quantitative measurement that can benefit brain areas that are consistently difficult to measure on diffusion-based methods, e.g., the grey matter (GM). The present Slicer extension proposes offer an image processing technique using the principle of diffusion distribution evaluation regarding the LMC complexity measure, named Diffusion Complexity (DC).
More details could be found in the documentation page
We are glad to know whether the DC mapping was helpful for your study or application. In that case, please cite at least on the following references:
da S. Senra Filho, A., Murta Junior, L. & Monteiro Paschoal, A. Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis. Magn Reson Mater Phy (2024). DOI
- Antonio Senra Filho: [email protected]
Please, note that this code is under the following license
- MIT License - Read file: LICENSE