Deep learning-based 3D MRI contrast-enhanced synthesis from a 2D noncontrast T2Flair sequence

Purpose Gadolinium-based contrast agents (GBCAs) have been successfully applied in magnetic resonance (MR) imaging to facilitate better lesion visualization. However, gadolinium deposition in the human brain raised widespread concerns recently. On the other hand, although high-resolution three-dimensional (3D) MR images are more desired for most existing medical image processing algorithms, their long scan duration and high acquiring costs make 2D MR images still much more common clinically. Therefore, developing alternative solutions for 3D contrast-enhanced MR image synthesis to replace GBCAs injection becomes an urgent requirement. Methods This study proposed a deep learning framework that produces 3D isotropic full-contrast T2Flair images from 2D anisotropic noncontrast T2Flair image stacks. The super-resolution (SR) and contrast-enhanced (CE) synthesis tasks are completed in sequence by using an identical generative adversarial network (GAN) with the same techniques. To solve the problem that intramodality datasets from different scanners have specific combinations of orientations, contrasts, and resolutions, we conducted a region-based data augmentation technique on the fly during training to simulate various imaging protocols in the clinic. We further improved our network by introducing atrous spatial pyramid pooling, enhanced residual blocks, and deep supervision for better quantitative and qualitative results. Results Our proposed method achieved superior CE-synthesized performance in quantitative metrics and perceptual evaluation. In detail, the PSNR, structural-similarity-index, and AUC are 32.25 dB, 0.932, and 0.991 in the whole brain and 24.93 dB, 0.851, and 0.929 in tumor regions. The radiologists' evaluations confirmed that our proposed method has high confidence in the diagnosis. Analysis of the generalization ability showed that benefiting from the proposed data augmentation technique, our network can be applied to unseen datasets with slight drops in quantitative and qualitative results. Conclusion Our work demonstrates the clinical potential of synthesizing diagnostic 3D isotropic CE brain MR images from a single 2D anisotropic noncontrast sequence.

Automated summary

This study proposed a deep learning framework to synthesize 3D full-contrast MR images from 2D images, addressing the issues caused by GBCAs injection. The improved network showed excellent performance in quantitative and qualitative evaluations, instilling high confidence in diagnosis.