Real-time diagnosis of intracerebral hemorrhage after thrombectomy is crucial for follow-up treatment. However, this is difficult to achieve with standard single-energy CT (SECT) due to similar CT values of blood and contrast agents under a single energy spectrum. In contrast, dual-energy CT (DECT) scanners employ two different energy spectra, which allows for real-time differentiation between hemorrhage and contrast extravasation based on energy-related attenuation characteristics. Unfortunately, DECT scanners are not as widely used as SECT scanners due to their high costs. To address this dilemma, in this paper, we generate pseudo DECT images from a SECT image for real-time diagnosis of hemorrhage. More specifically, we propose a SECT-to-DECT Transformer-based Generative Adversarial Network (SDTGAN), which is a 3D transformer-based multi-task learning framework equipped with a shared attention mechanism. In this way, SDTGAN can be guided to focus more on high-density areas (crucial for hemorrhage diagnosis) during the generation. Meanwhile, the introduced multi-task learning strategy and the shared attention mechanism also enable SDTGAN to model dependencies between interconnected generation tasks, improving generation performance while significantly reducing model parameters and computational complexity. In the experiments, we approximate real SECT images using mixed 120kV images from DECT data to address the issue of not being able to obtain the true paired DECT and SECT data. Extensive experiments demonstrate that SDTGAN can generate DECT images better than state-of-the-art methods. The code of our implementation is available at .

中文翻译：

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单能CT生成双能CT实时诊断脑出血


取栓术后脑出血的实时诊断对于后续治疗至关重要。然而，由于单能谱下血液和造影剂的 CT 值相似，使用标准单能 CT (SECT) 很难实现这一点。相比之下，双能 CT (DECT) 扫描仪采用两种不同的能谱，可以根据能量相关的衰减特性实时区分出血和造影剂外渗。不幸的是，由于 DECT 扫描仪成本较高，其使用不如 SECT 扫描仪广泛。为了解决这个难题，在本文中，我们从 SECT 图像生成伪 DECT 图像，用于实时诊断出血。更具体地说，我们提出了一种基于 SECT 到 DECT Transformer 的生成对抗网络（SDTGAN），它是一种基于 3D Transformer 的多任务学习框架，配备了共享注意力机制。这样，可以引导SDTGAN在生成过程中更多地关注高密度区域（对于出血诊断至关重要）。同时，引入的多任务学习策略和共享注意力机制也使得SDTGAN能够对互连的生成任务之间的依赖关系进行建模，提高生成性能，同时显着降低模型参数和计算复杂度。在实验中，我们使用 DECT 数据的混合 120kV 图像来近似真实的 SECT 图像，以解决无法获得真实配对的 DECT 和 SECT 数据的问题。大量实验表明，SDTGAN 可以比最先进的方法更好地生成 DECT 图像。我们的实现代码可以在 处找到。