Improved deep learning-based IVIM parameter estimation via the use of more “realistic” simulated brain data

Abstract

Background

Due to the low signal-to-noise ratio (SNR) and the limited number of b-values, precise parameter estimation of intravoxel incoherent motion (IVIM) imaging remains an open issue to date, especially for brain imaging where the relatively small difference between D and D^* easily leads to outliers and obvious graininess in estimated results.

Purpose

To propose a synthetic data driven supervised learning method (SDD-IVIM) for improving precision and noise robustness in IVIM parameter estimation without relying on real-world data for neural network training.

Methods

On account of the absence of standard IVIM parametric maps from real-world data, a novel model-based method for generating synthetic human brain IVIM data was introduced. Initially, the parameter values of synthetic IVIM parametric maps were sampled from the complex distributions composed of a series of simple and uniform distributions. Subsequently, these parametric maps were modulated with human brain texture to imitate brain tissue structure. Finally, they were used to generate synthetic human brain multi-b-value diffusion-weighted (DW) images based on the IVIM bi-exponential model. With the proposed data synthesis method, an ordinary U-Net with spatial smoothness was employed for IVIM parameter mapping within a supervised learning framework. The performance of SDD-IVIM was evaluated on both numerical phantom and 20 glioma patients. The estimated IVIM parametric maps were compared to those derived from five state-of-the-art methods.

Results

In numerical phantom experiments, SDD-IVIM method produces IVIM parametric maps with lower mean absolute error, lower mean bias, and higher structural similarity compared to the other five methods, especially when the SNR of DW images is low. In glioma patient experiments, SDD-IVIM method offers lower coefficient of variation and more reasonable contrast-to-noise ratio between tumor and contralateral normal appearing white matter than the other five methods.

Conclusion

Our method owns superior performance in parametric map quality, parameter estimation precision, and lesion characterization in IVIM parameter estimation, with strong resistance to noise.