Model-free, regularized, fast, and robust analytical orientation distribution function estimation.

Area of Science:

• Neuroimaging
• Diffusion MRI
• White Matter Microstructure

Background:

• Diffusion Tensor Imaging (DTI) has limitations in exploring complex white matter micro-structure.
• High Angular Resolution Imaging (HARDI) offers superior micro-structure exploration using Orientation Distribution Functions (ODFs).
• Existing ODF estimation methods from HARDI data often rely on assumptions and introduce modeling errors.

Purpose of the Study:

• To develop a uniform analytical method for estimating two types of ODFs (QBI and DSI) from HARDI data.
• To overcome limitations of existing methods, such as assumptions and modeling errors.
• To provide an accurate ODF estimation method applicable to various HARDI data.

Main Methods:

• Proposed a novel analytical method based on Spherical Polar Fourier Expression (SPFE) of diffusion MRI signals in q-space.
• The method performs a linear transformation from q-space signals to ODFs represented by Spherical Harmonics (SH).
• The approach naturally integrates multi-shell HARDI data and avoids Funk-Radon Transform blurring errors.

Main Results:

• The proposed method accurately estimates ODFs with minimal assumptions.
• It successfully combines diffusion MRI signals from different q-shells.
• Validation on synthetic, phantom, and real data demonstrated robust performance, especially in low SNR and complex micro-structure scenarios.

Conclusions:

• The developed uniform analytical method provides an assumption-free approach for ODF estimation in HARDI.
• This technique improves the analysis of white matter microstructure, particularly in challenging imaging conditions.
• The method offers a significant advancement over existing ODF reconstruction techniques.