Deep learning‐based reconstruction on intensity‐inhomogeneous diffusion magnetic resonance imaging

Ultra high field diffusion magnetic resonance imaging (dMRI) provides diffusion‐weighted (DW) images with a high signal‐to‐noise ratio, but increases inhomogeneity, which affects the accuracy of dMRI metric reconstruction. Current methods for correcting inhomogeneity rarely consider the accuracy of the reconstructed dMRI metrics. Deep learning models for reconstructing metrics from dMRI signals typically assume that DW images have a homogeneous intensity. To address these challenges, we propose a deep learning model capable of directly reconstructing high‐accuracy dMRI metric maps from inhomogeneous DW images.

An attention‐based q‐space inhomogeneity‐resistant reconstruction network (qIRR‐Net) is proposed for the voxel‐wise reconstruction of diffusion tensor imaging and diffusion kurtosis imaging metrics. A training procedure based on data augmentation and consistency loss is introduced to ensure that the reconstruction results of qIRR‐Net are not affected by signal inhomogeneity. The 3T and 7T dMRI data from the Human Connectome Project are used for model training, testing, and evaluation.

On the 3T dMRI data with simulated inhomogeneity, qIRR‐Net improves the peak signal‐to‐noise ratio by 5.39 and the structural similarity index measure by 0.18 compared with weighted linear least‐squares fitting. On the 7T dMRI data, the metric maps reconstructed by qIRR‐Net not only exhibit clearer tissue structures but also demonstrate greater stability compared with the weighted linear least‐squares results.

The proposed qIRR‐Net enables the accurate reconstruction of dMRI metrics from inhomogeneous DW images. This approach could potentially be expanded to obtain multiple artifact‐free metric maps from ultrahigh field dMRI for neuroscience research and neurology applications.