Abstract
Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a medical imaging technique that plays a crucial role in the detailed visualization and identification of tissue perfusion in abnormal lesions and radiological suggestions for biopsy. However, DCE-MRI involves the administration of a Gadolinium based (Gad) contrast agent, which is associated with a risk of toxicity in the body. Previous deep learning approaches that synthesize DCE-MR images employ unimodal non-contrast or low-dose contrast MRI images lacking focus on the local perfusion information within the anatomy of interest. We propose AAD-DCE, a generative adversarial network (GAN) with an aggregated attention discriminator module consisting of global and local discriminators. The discriminators provide a spatial embedded attention map to drive the generator to synthesize early and late response DCE-MRI images. Our method employs multimodal inputs - T2 weighted (T2W), Apparent Diffusion Coefficient (ADC), and T1 pre-contrast for image synthesis. Extensive comparative and ablation studies on the ProstateX dataset show that our model (i) is agnostic to various generator benchmarks and (ii) outperforms other DCE-MRI synthesis approaches with improvement margins of +0.64 dB PSNR, +0.0518 SSIM, -0.015 MAE for early response and +0.1 dB PSNR, +0.0424 SSIM, -0.021 MAE for late response, and (ii) emphasize the importance of attention ensembling. Our code is available at this https URL.
Abstract (translated)
动态对比增强磁共振成像（DCE-MRI）是一种医学影像技术，在异常病灶和放射学活检建议中的组织灌注的详细可视化与识别中起着关键作用。然而，进行DCE-MRI需要使用基于钆（Gadolinium, Gad）的对比剂，这种物质在体内存在毒性风险。以往采用深度学习方法合成DCE-MR图像时，仅依赖单一模态的非增强或低剂量增强MRI图像，并且未充分关注感兴趣解剖结构内的局部灌注信息。 我们提出了一种名为AAD-DCE的方法，这是一种生成对抗网络（GAN），其包含一个集成了全局和局部判别器的聚合注意力判别模块。该判别器提供了一个空间嵌入注意图，以驱动生成器合成早期响应和晚期响应DCE-MRI图像。我们的方法采用多模态输入——T2加权（T2W）、表观扩散系数（ADC）及T1平扫前的影像进行图像合成。 在ProstateX数据集上的广泛比较与消融研究显示，我们的模型： (i) 对各种生成器基准是无差别的； (ii) 在早期响应和晚期响应分别提高了+0.64 dB PSNR、+0.0518 SSIM和-0.015 MAE以及+0.1 dB PSNR、+0.0424 SSIM和-0.021 MAE，优于其他DCE-MRI合成方法； (ii) 强调了注意力集成的重要性。 我们的代码可在提供的链接中获取。
URL
https://arxiv.org/abs/2502.02555
