Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure.

Area of Science:

• Neuroimaging
• Biophysics
• Medical Physics

Background:

• Diffusion-weighted magnetic resonance (MR) signals offer insights into tissue microstructure.
• Existing methods like diffusion tensor imaging (DTI) have limitations in capturing complex diffusion patterns.

Purpose of the Study:

• To develop a quantitative, efficient, and robust framework for representing diffusion-weighted MR imaging (MRI) data.
• To define and map novel quantitative descriptors of diffusion using the Mean Apparent Propagator MRI (MAP-MRI) framework.
• To capture complex diffusion characteristics beyond Gaussian models.

Main Methods:

• Developed an analytical representation of the 3D q-space MR signal using a basis function expansion.
• Introduced the Mean Apparent Propagator (MAP) model to describe molecular displacements.
• Computed novel diffusion descriptors including return-to-origin probabilities (RTOP, RTPP, RTAP).

Main Results:

• MAP-MRI framework accurately represents diffusion in complex geometries.
• Higher-order terms in the expansion enable reconstruction of the true average propagator and orientational distribution function (ODF).
• Demonstrated novel parameters capturing diffusion anisotropy and non-Gaussian diffusion.
• Experiments on marmoset brain revealed previously obscured microstructural features.

Conclusions:

• MAP-MRI provides a comprehensive framework for modeling 3D q-space signals and diffusion propagators.
• Novel quantifiable parameters from MAP-MRI offer deeper insights into nervous tissue microstructure.
• This approach holds promise for investigating normal and pathologic nervous tissue organization.