High-dose contrast-enhanced MRI in multiple sclerosis

Deep Learning-Based 3D MRI Contrast-Enhanced Synthesis From A 2D Non-contrast T2Flair Sequence Deep Learning, MR images Synthesis

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 images 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 non-contrast 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 intra-modality 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. Detailedly, the PSNR, SSIM 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 non-contrast sequence. This article is protected by copyright. All rights reserved.

A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI

PURPOSE

With rising safety concerns over the use of gadolinium-based contrast agents (GBCAs) in contrast-enhanced MRI, there is a need for dose reduction while maintaining diagnostic capability. This work proposes comprehensive technical solutions for a deep learning (DL) model that predicts contrast-enhanced images of the brain with approximately 10% of the standard dose, across different sites and scanners.

METHODS

The proposed DL model consists of a set of methods that improve the model robustness and generalizability. The steps include multi-planar reconstruction, 2.5D model, enhancement-weighted L1, perceptual, and adversarial losses. The proposed model predicts contrast-enhanced images from corresponding pre-contrast and low-dose images. With IRB approval and informed consent, 640 heterogeneous patient scans (56 train, 13 validation, and 571 test) from 3 institutions consisting of 3D T1-weighted brain images were used. Quantitative metrics were computed and 50 randomly sampled test cases were evaluated by 2 board-certified radiologists. Quantitative tumor segmentation was performed on cases with abnormal enhancements. Ablation study was performed for systematic evaluation of proposed technical solutions.

RESULTS

The average peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) between full-dose and model prediction were 35.07 ± 3.84 dB and 0.92 ± 0.02 , respectively. Radiologists found the same enhancing pattern in 45/50 (90%) cases; discrepancies were minor differences in contrast intensity and artifacts, with no effect on diagnosis. The average segmentation Dice score between full-dose and synthesized images was 0.88 ± 0.06 (median = 0.91).

CONCLUSIONS

We have proposed a DL model with technical solutions for low-dose contrast-enhanced brain MRI with potential generalizability under diverse clinical settings.