Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3650-3653. doi: 10.1109/EMBC46164.2021.9630887.
In clinical practice, about 35% of MRI scans are enhanced with Gadolinium - based contrast agents (GBCAs) worldwide currently. Injecting GBCAs can make the lesions much more visible on contrast-enhanced scans. However, the injection of GBCAs is high-risk, time-consuming, and expensive. Utilizing a generative model such as an adversarial network (GAN) to synthesize the contrast-enhanced MRI without injection of GBCAs becomes a very promising alternative method. Due to the different features of the lesions in contrast-enhanced images while the single-scale feature extraction capabilities of the traditional GAN, we propose a new generative model that a multi-scale strategy is used in the GAN to extract different scale features of the lesions. Moreover, an attention mechanism is also added in our model to learn important features automatically from all scales for better feature aggregation. We name our proposed network with an attention-based multi-scale contrasted-enhanced-image generative adversarial network (AMCGAN). We examine our proposed AMCGAN on a private dataset from 382 ankylosing spondylitis subjects. The result shows our proposed network can achieve state-of-the-art in both visual evaluations and quantitative evaluations than traditional adversarial training.Clinical Relevance-This study provides a safe, convenient, and inexpensive tool for the clinical practices to get contrast-enhanced MRI without injection of GBCAs.
在临床实践中，目前全球约有 35%的 MRI 扫描采用钆基对比剂（GBCA）增强。注射 GBCA 可以使病变在对比增强扫描中更加明显。然而，注射 GBCA 存在高风险、耗时且昂贵的问题。利用生成式模型（如对抗网络（GAN））在不注射 GBCA 的情况下合成对比增强 MRI 成为一种非常有前途的替代方法。由于病变在对比增强图像中的特征不同，而传统 GAN 的单尺度特征提取能力有限，我们提出了一种新的生成式模型，即在 GAN 中使用多尺度策略提取病变的不同尺度特征。此外，我们的模型还添加了注意力机制，以便从所有尺度自动学习重要特征，从而更好地进行特征聚合。我们将提出的网络命名为基于注意力的多尺度对比增强图像生成对抗网络（AMCGAN）。我们在一个来自 382 名强直性脊柱炎患者的私有数据集上评估了我们提出的 AMCGAN。结果表明，与传统的对抗训练相比，我们提出的网络在视觉评估和定量评估方面都达到了最先进的水平。临床相关性-这项研究为临床实践提供了一种安全、方便和经济的工具，无需注射 GBCA 即可获得对比增强 MRI。
