IEEE Trans Biomed Eng. 2023 Feb;70(2):401-412. doi: 10.1109/TBME.2022.3192309. Epub 2023 Jan 19.
Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically.
In this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors.
Extensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24 dB in the brain and 21.2 dB in tumor regions.
Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning.
钆基造影剂（GBCA）已广泛用于改善脑磁共振成像（MRI）中的疾病可视化。然而，脑内和体内的钆沉积引起了人们对 GBCA 使用的安全性担忧。因此，开发能够降低甚至消除 GBCA 暴露的新方法，同时提供类似的对比信息，在临床上将具有重要意义。
在这项工作中，我们提出了一种基于深度学习的脑肿瘤患者对比增强 T1 合成方法。我们设计了一个 3D 高分辨率全卷积网络（FCN），通过处理和并行聚合多尺度信息来保持高分辨率信息，以将对比前 MRI 序列映射到对比增强 MRI 序列。具体来说，将三个对比前的 MRI 序列（T1、T2 和表观扩散系数图（ADC）作为输入，将对比后的 T1 序列作为目标输出。为了缓解正常组织和肿瘤区域之间的数据不平衡问题，我们引入了局部损失来提高肿瘤区域的贡献，从而在肿瘤区域获得更好的增强效果。
我们进行了广泛的定量和可视化评估，所提出的模型在大脑中的 PSNR 为 28.24dB，在肿瘤区域中的 PSNR 为 21.2dB。
我们的结果表明，通过深度学习生成的合成对比图像可能替代 GBCA。
