ActiveAx_ADD : Toward non-parametric and orientationally invariant axon diameter distribution mapping using PGSE.

Area of Science:

• Neuroimaging
• Biophysics
• Medical Physics

Background:

• Non-invasive axon diameter distribution (ADD) mapping using diffusion MRI is challenging due to the ill-posed nature of the problem.
• Current methods often require prior knowledge of axon orientation, limiting their applicability.
• Existing frameworks like AMICO focus on mean diameter estimation, necessitating advancements for full distribution analysis.

Purpose of the Study:

• To develop ActiveAx_ADD, a novel method for non-parametric and orientationally invariant estimation of the whole axon diameter distribution (ADD).
• To extend the ActiveAx framework to provide a more comprehensive characterization of white matter microstructure.
• To address the limitations of existing ADD mapping techniques in diffusion MRI.

Main Methods:

• Implementation of ActiveAx_ADD utilizing Laplacian regularization for robust ADD estimation.
• Evaluation through Monte Carlo simulations on synthetic white matter samples.
• Comparison with existing microstructure imaging approaches.

Main Results:

• ActiveAx_ADD successfully reconstructed robust ADDs when analyzing isolated intra-axonal signals.
• The method demonstrated orientationally invariant estimation capabilities.
• Challenges were identified when incorporating the extra-axonal compartment, leading to spurious peaks and increased variability.

Conclusions:

• Laplacian regularization effectively addresses the ill-posedness associated with the intra-axonal compartment in ADD mapping.
• ActiveAx_ADD shows promise for non-parametric, orientationally invariant ADD estimation from isolated intra-axonal signals.
• Further research is crucial to overcome the difficulties in disentangling intra- and extra-axonal contributions for clinical applications.