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

Abstract

Objective.Diffusion-weighted MRI (DW-MRI) is used to quantitatively characterize the microscopic structure of muscle through anisotropic water diffusion in soft tissue. Applications such as tumor propagation modeling require precise detection of muscle fiber orientation. That is, the direction along the fibers that coincides with the direction of the principal eigenvector of the diffusion tensor reconstructed from DW-MRI data. For clinical applications, the quality of image data is determined by the signal-to-noise ratio (SNR) that must be achieved within the appropriate scan time. The acquisition protocol must therefore be optimized. This implies the need for SNR criteria that match the data quality of the application.Approach.Muscles with known structural heterogeneity, e.g. bipennate muscles such as the rectus femoris in the thigh, provide a natural quality benchmark to determine accuracy of inferred fiber orientation at different scan parameters. In this study, we analyze DW-MR images of the thigh of a healthy volunteer at different SNRs and use PCA to identify subsets of voxels with different directions of diffusion tensor eigenvectors corresponding to different pennate angles. We propose to use the separation index of spatial co-localization of the clustered eigenvectors as a quality metric for fiber orientation detection.Main results.The clustering in the PCA component coordinates can be translated to the separation of the two compartments of the bipennate muscle on either side of the central tendon according to the pennate angle. The separation index reflects the degree of the separation and is a function of SNR.Significance.Because the separation index allows joint estimation of spatial and directional noise in DW-MRI as a single parameter, it will allow future quantitative optimization of DW-MRI soft tissue protocols.