Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures

Visualization of subcortical gray matter is essential in neuroscience and clinical practice, particularly for disease understanding and surgical planning. Although multi-inversion time (multi-TI) $T_1$ -weighted ( $T_1$ -w) magnetic resonance (MR) imaging improves visualization, it is only acquired in specific clinical settings and not available in common public MR datasets.

We present SyMTIC (synthetic multi-TI contrasts), a deep learning method that generates synthetic multi-TI images using routinely acquired $T_1$ -w, $T_2$ -weighted ( $T_2$ -w), and fluid-attenuated inversion recovery (FLAIR) images. Our approach combines image translation via deep neural networks with imaging physics to estimate longitudinal relaxation time ( $T1$ ) and proton density ( $\rho$ ) maps. These maps are then used to compute multi-TI images with arbitrary inversion times.

SyMTIC was trained using paired magnetization prepared rapid acquisition with gradient echo (MPRAGE) and fast gray matter acquisition T1 inversion recovery (FGATIR) images along with $T_2$ -w and FLAIR images. It accurately synthesized multi-TI images from standard clinical inputs, achieving image quality comparable to that from explicitly acquired multi-TI data. The synthetic images, especially for TI values between 400 to 800 ms, enhanced visualization of subcortical structures and improved segmentation of thalamic nuclei.

SyMTIC enables robust generation of high-quality multi-TI images from routine MR contrasts. When paired with the HACA3 algorithm, it generalizes well to varied clinical datasets, including those without FLAIR or $T_2$ -w images and unknown parameters, offering a practical solution for improving brain MR image visualization and analysis.