AAD-DCE：一种用于早期和晚期动态对比增强前列腺MRI合成的聚合多模态注意力机制
AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI Synthesis
AI总结：
本文提出AAD-DCE生成对抗网络，通过聚合全局与局部判别器的注意力机制，利用T2W、ADC和T1对比前多模态输入合成早期和晚期DCE-MRI图像，在ProstateX数据集上优于现有方法并验证了注意力集成的重要性。
摘要：
动态对比增强磁共振成像（DCE-MRI）是一种医学成像技术，在异常病变组织灌注的详细可视化和识别以及放射学活检建议中发挥着关键作用。然而，DCE-MRI涉及使用基于钆（Gad）的对比剂，这与体内毒性风险相关。以往合成DCE-MR图像的深度学习方法采用单模态非对比或低剂量对比MRI图像，缺乏对感兴趣解剖区域内局部灌注信息的关注。我们提出AAD-DCE，一种生成对抗网络（GAN），其具有由全局和局部判别器组成的聚合注意力判别器模块。这些判别器提供空间嵌入注意力图，以驱动生成器合成早期和晚期响应的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）和T1对比前图像用于图像合成。在ProstateX数据集上进行的广泛比较和消融研究表明，我们的模型（i）对各种生成器基准具有不可知性，并且（ii）优于其他DCE-MRI合成方法，早期响应的改进幅度为+0.64 dB PSNR、+0.0518 SSIM、-0.015 MAE，晚期响应的改进幅度为+0.1 dB PSNR、+0.0424 SSIM、-0.021 MAE，以及（ii）强调了注意力集成的重要性。我们的代码可在https://github.com/bhartidivya/AAD-DCE获取。
英文摘要：
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 https://github.com/bhartidivya/AAD-DCE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
