Internal gradient distributions: A susceptibility-derived tensor delivering morphologies by magnetic resonance

Area of Science:

• Physics
• Biophysics
• Materials Science

Background:

• Nuclear Magnetic Resonance (NMR) is crucial for structural analysis.
• Magnetic Resonance Imaging (MRI), derived from NMR, is a key non-invasive diagnostic tool.
• Current MRI spatial resolution is limited, often to tens of micrometers, hindering detailed morphological studies.

Purpose of the Study:

• To develop a new diffusion-based method for enhanced morphological sensing in Magnetic Resonance Imaging (MRI).
• To achieve morphological parameter mapping in the nanometer-to-millimeter range.
• To overcome the spatial resolution limitations of conventional MRI.

Main Methods:

• Exploitation of isotropic or anisotropic diffusion processes.
• Utilizing distributions of susceptibility-induced magnetic field gradients.
• Development of a theoretical framework for internal gradient-distribution tensors.
• Design of gradient-based spin-echo sequences for measuring new observables.

Main Results:

• Demonstration of a new method sensing morphological parameters from nm-mm range.
• Successful mapping of orientations using diffusion, even with unconstrained diffusion.
• Validation of the method on structured systems, including biological tissues.

Conclusions:

• The proposed method significantly enhances morphological information extraction in MRI.
• Internal gradient-distribution tensors provide a novel source of morphological data.
• This technique offers improved spatial resolution and orientation mapping capabilities for complex systems.