Synthetic X-Q space learning for diffusion MRI parameter estimation: a pilot study in breast DKI.

ABSTRACT:

Purpose

For diffusion MRI (dMRI) parameter estimation, machine-learning approaches have shown promising results so far including the synthetic Q-space learning (synQSL) based on regressor training with only synthetic data. In this study, we aimed at the development of a new method named synthetic X-Q space learning (synXQSL) to improve robustness and investigated the basic characteristics.

Methods

For training data, local parameter patterns of 3 × 3 voxels were synthesized by a linear combination of six bases, in which parameters are estimated at the center voxel. We prepared three types of local patterns by choosing the number of bases: flat, linear and quadratic. Then, at each location of 3 × 3 voxels, signal values of the diffusion-weighted image were computed by the sign

SUBMITTER: Masutani Y 

PROVIDER: S-EPMC12689713 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature