Tensor Decomposition-Based Multi-Signal Matrix Pencil Method for Myelin Water Fraction Estimation

To develop a robust method for estimating myelin water fraction (MWF) from multi-echo gradient-recalled echo (mGRE) data under acquisition regimes that limit echo-train length and support higher spatial sampling.

A tensor decomposition-based multi-signal matrix pencil (T-MP) framework is proposed to incorporate data-driven spatial information from neighboring voxels into MWF estimation. By leveraging the reduced temporal sampling requirements of matrix pencil-based approaches, the method enables stable parameter estimation with fewer echoes compared to conventional iterative fitting techniques. The performance of the proposed method was evaluated using numerical simulations across a range of signal-to-noise ratios and echo spacings, as well as in vivo mGRE datasets acquired at different spatial resolutions with shortened echo trains.

Numerical simulations demonstrate that accurate MWF estimation can be achieved with substantially fewer temporal samples, facilitating acquisition protocols that prioritize spatial encoding. In vivo experiments show that the proposed method provides consistent MWF maps across different spatial resolutions without qualitative degradation. Kernel density analysis reveals improved estimation consistency in both white and gray matter compared with conventional voxel-wise fitting approaches. In addition, the proposed framework substantially reduces per-slice computation time.

A tensor decomposition-based multi-signal matrix pencil method for MWF estimation is presented that integrates spatially informed signal structure while reducing temporal sampling requirements. The proposed framework supports spatially efficient mGRE acquisitions and provides improved robustness and computational efficiency compared to existing voxel-wise approaches.