Unmixing tissue compartments via deep learning T1-T2-relaxation correlation imaging

Magnetic resonance imaging is a versatile diagnostic tool with numerous clinical applications. However, despite advances towards higher resolutions, it cannot resolve images on a cellular level. To nevertheless probe tissue microstructure, multidimensional correlation imaging emerges as a promising method. It takes advantage of the fact that each tissue compartment has a unique signal. Usually, these multi-compartmental characteristics are averaged over a macroscopic voxel. In contrast, correlation imaging aims to probe the true, heterogeneous nature of tissue. Based on image series acquired with varying inversion time TI and echo time TE, multiparametric spectra of T1 and T2 relaxation times in every voxel can be reconstructed, revealing sub-voxel tissue compartments. However, even with impractically long acquisition times spent on dense sampling of the image (3D) and TI-TE-space (2D), the inverse problem of retrieving these components from measured signal curves remains highly ill-conditioned and requires expensive regularized approaches. We formulate multiparametric correlation imaging as a classification problem and propose a flexible physicsinformed deep learning framework comprising a multilayer perceptron. This way, we efficiently reconstruct voxel-wise T1-T2-spectra with increased robustness to noise and undersampling in the TI-TE-space compared to state-of-the-art regression. Our results show feasibility of further acceleration of the acquisition by a factor of 4. After training on synthetic data that is not constraint by pre-defined tissue classes and independent of annotated data, we test our method on in-vivo brain data, revealing sub-voxel compartments in white and gray matter. This allows us to quantify tissue microstructure and will potentially lead to novel biomarkers.