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

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.

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 signal model equation for diffusional kurtosis imaging and Rician noise simulation. The multi-layer perceptron was used for parameter estimation and was trained for each parameter with various noise levels. The level is controlled by a noise ratio defined as a fraction of the standard deviation in the Rician noise distribution normalized by the average b = 0 signal values. Experiments for visual and quantitative validation were performed with synthetic data, a digital phantom and clinical breast datasets in comparison with the previous methods.

By using synthetic datasets, synXQSL outperformed synQSL in the parameter estimation of noisy data sets. Through the digital phantom experiments, the combination of synXQSL bases yields different results and a quadratic pattern could be the reasonable choice. The clinical data experiments indicate that synXQSL suppresses noises in estimated parameter maps and consequently brings higher contrast.

The basic characteristics of synXQSL were investigated by using various types of datasets. The results indicate that synXQSL with the appropriate choice of bases in training data synthesis has the potential to improve dMRI parameters in noisy datasets.