一种名为图像引导放射治疗的放射治疗技术采用整个治疗过程中频繁的成像。基于扇形束计算机断层摄影（FBCT）的规划，接着是基于圆锥束计算机断层扫描（CBCT）的放射治疗递送，极大地提高了治疗的准确性。如果CBCT能够取代FBCT，那么在辐射暴露和成本方面都可以进一步前进。本文提出了一种用于CBCT-to-FBCT合成的条件生成对抗网络（CGAN）。具体来说，引入了一种新的结构称为Nested Residual UNet （NR-UNet）作为CGAN的生成器。在生成器中使用了组合损失函数，其中包括对抗性损失，均方误差（MSE）和梯度差异损失（GDL）。CGAN通过采用三个连续的CBCT切片来利用输入中的切片间相关性来生成FBCT切片。该模型使用53名癌症患者的头颈（H＆N）FBCT-CBCT图像进行训练。合成图像显示峰值信噪比为34.04±0.93分贝，结构相似性指数测量值为0.9751±0.001，平均绝对误差为14.81±4.70 HU。平均而言，所提出的模型保证了对比噪声比比输入CBCT图像提高了四倍。该模型还最小化了均方误差并减轻了模糊度。与基于CBCT的计划相比，合成图像得出的治疗计划更接近于基于FBCT的计划。三个切片到单个切片的转换捕捉到了输入中的三维相关信息。此外，它能够承受与三维图像合成模型相关的计算复杂性。此外，结果表明，所提出的模型比最先进的方法更优越。©2023年。大洋洲物理科学和医学工程师学院。

A radiotherapy technique called Image-Guided Radiation Therapy adopts frequent imaging throughout a treatment session. Fan Beam Computed Tomography (FBCT) based planning followed by Cone Beam Computed Tomography (CBCT) based radiation delivery drastically improved the treatment accuracy. Furtherance in terms of radiation exposure and cost can be achieved if FBCT could be replaced with CBCT. This paper proposes a Conditional Generative Adversarial Network (CGAN) for CBCT-to-FBCT synthesis. Specifically, a new architecture called Nested Residual UNet (NR-UNet) is introduced as the generator of the CGAN. A composite loss function, which comprises adversarial loss, Mean Squared Error (MSE), and Gradient Difference Loss (GDL), is used with the generator. The CGAN utilises the inter-slice dependency in the input by taking three consecutive CBCT slices to generate an FBCT slice. The model is trained using Head-and-Neck (H&N) FBCT-CBCT images of 53 cancer patients. The synthetic images exhibited a Peak Signal-to-Noise Ratio of 34.04±0.93 dB, Structural Similarity Index Measure of 0.9751±0.001 and a Mean Absolute Error of 14.81±4.70 HU. On average, the proposed model guarantees an improvement in Contrast-to-Noise Ratio four times better than the input CBCT images. The model also minimised the MSE and alleviated blurriness. Compared to the CBCT-based plan, the synthetic image results in a treatment plan closer to the FBCT-based plan. The three-slice to single-slice translation captures the three-dimensional contextual information in the input. Besides, it withstands the computational complexity associated with a three-dimensional image synthesis model. Furthermore, the results demonstrate that the proposed model is superior to the state-of-the-art methods.© 2023. Australasian College of Physical Scientists and Engineers in Medicine.