Physics-guided self-supervised learning for retrospective T₁ and T₂ mapping from conventional weighted brain MRI:...

To develop a self-supervised learning method to retrospectively estimate T₁ and T₂ values from clinical weighted MRI.

A self-supervised learning approach was constructed to estimate T₁, T₂, and proton density maps from conventional T₁- and T₂-weighted images. MR physics models were employed to regenerate the weighted images from the network outputs, and the network was optimized based on loss calculated between the synthesized and input weighted images, alongside additional constraints based on prior information. The method was evaluated on healthy volunteer data, with conventional mapping as references. The reproducibility was examined on two 3.0T scanners. Performance in tumor characterization was inspected by applying the method to a public glioblastoma dataset.

For T₁ and T₂ estimation from three weighted images (T₁ MPRAGE, T₁ gradient echo sequences, and T₂ turbo spin echo), the deep learning method achieved global voxel-wise error ≤9% in brain parenchyma and regional error ≤12.2% in six types of brain tissues. The regional measurements obtained from two scanners showed mean differences ≤2.4% and correlation coefficients >0.98, demonstrating excellent reproducibility. In the 50 glioblastoma patients, the retrospective quantification results were in line with literature reports from prospective methods, and the T₂ values were found to be higher in tumor regions, with sensitivity of 0.90 and specificity of 0.92 in a voxel-wise classification task between normal and abnormal regions.

The self-supervised learning method is promising for retrospective T₁ and T₂ quantification from clinical MR images, with the potential to improve the availability of quantitative MRI and facilitate brain tumor characterization.