Anatomy-based diffusion-weighted MRI quality metric: a proof-of-concept for deriving accurate muscle fiber...

Area of Science:

• Biomedical Imaging
• Soft Tissue Characterization
• Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI)

Background:

• DW-MRI quantitatively characterizes muscle microstructure via anisotropic water diffusion.
• Accurate muscle fiber orientation detection is crucial for applications like tumor propagation modeling.
• Image quality, determined by signal-to-noise ratio (SNR), must be optimized for clinical DW-MRI protocols.

Purpose of the Study:

• To develop and validate a quality metric for assessing muscle fiber orientation detection accuracy in DW-MRI.
• To establish SNR criteria for optimizing DW-MRI soft tissue acquisition protocols.

Main Methods:

• Analysis of DW-MRI thigh images from a healthy volunteer at varying SNRs.
• Utilized Principal Component Analysis (PCA) to identify voxels with distinct diffusion tensor eigenvector directions.
• Proposed a 'separation index' based on the spatial co-localization of clustered eigenvectors as a quality metric.

Main Results:

• PCA clustering correlated with the separation of muscle compartments in a bipennate muscle (rectus femoris) based on pennate angles.
• The proposed separation index effectively quantifies this separation.
• The separation index was demonstrated to be a function of SNR.

Conclusions:

• The separation index provides a single parameter to jointly estimate spatial and directional noise in DW-MRI.
• This metric enables quantitative optimization of DW-MRI soft tissue protocols.
• Facilitates improved accuracy in muscle fiber orientation detection for clinical applications.