Synthesized myelin and iron stainings from 7T multi-contrast MRI via deep learning

Iron and myelin are key biomarkers for studying neurodegenerative and demyelinating brain diseases. Multi-contrast MRI techniques, such as R2* and QSM, are commonly used for iron assessment, with histology as the reference standard, but non-invasive myelin assessment remains challenging. To address this, we developed a deep learning model to generate iron and myelin staining images from in vivo multi-contrast MRI data, with a resolution comparable to ex vivo histology macro-scans. A cadaver head was scanned using a 7T MR scanner to acquire T1-weighted and multi-echo GRE data for R2*, and QSM processing, followed by histological staining for myelin and iron. To evaluate the generalizability of the model, a second cadaver head and two in vivo MRI datasets were included. After MRI-to-histology registration in the training subject, a self-attention generative adversarial network (GAN) was trained to synthesize myelin and iron staining images using various combinations of MRI contrast. The model achieved optimal myelin prediction when combining T1w, R2*, and QSM images. Incorporating the synthesized myelin images improved the subsequent prediction of iron staining. The generated images displayed fine details similar to those in histology data and demonstrated generalizability across healthy control subjects. Synthesized myelin images clearly differentiated myelin concentration between white and gray matter, while synthesized iron staining presented distinct patterns such as particularly high deposition in deep gray matter. This study shows that deep learning can transform MRI data into histological feature images, offering ex vivo insights from in vivo data and contributing to advancements in brain histology research.