The synthesis of computed tomography (CT) from magnetic resonance imaging (MRI) and cone-beam CT (CBCT) plays a critical role in clinical treatment planning by enabling accurate anatomical representation in adaptive radiotherapy. In this work, we propose GANeXt, a 3D patch-based, fully ConvNeXt-powered generative adversarial network for unified CT synthesis across different modalities and anatomical regions. Specifically, GANeXt employs an efficient U-shaped generator constructed from stacked 3D ConvNeXt blocks with compact convolution kernels, while the discriminator adopts a conditional PatchGAN. To improve synthesis quality, we incorporate a combination of loss functions, including mean absolute error (MAE), perceptual loss, segmentation-based masked MAE, and adversarial loss and a combination of Dice loss and cross-entropy for multi-head segmentation discriminator. For both tasks, training is performed with a batch size of 8 using two separate AdamW optimizers for the generator and discriminator, each equipped with a warmup and cosine decay scheduler, with learning rates of $5\times10^{-4}$ and $1\times10^{-3}$, respectively. Data preprocessing includes deformable registration, foreground cropping, percentile normalization for the input modality, and linear normalization of the CT to the range $[-1024, 1000]$. Data augmentation involves random zooming within $(0.8, 1.3)$ (for MRI-to-CT only), fixed-size cropping to $32\times160\times192$ for MRI-to-CT and $32\times128\times128$ for CBCT-to-CT, and random flipping. During inference, we apply a sliding-window approach with $0.8$ overlap and average folding to reconstruct the full-size sCT, followed by inversion of the CT normalization. After joint training on all regions without any fine-tuning, the final models are selected at the end of 3000 epochs for MRI-to-CT and 1000 epochs for CBCT-to-CT using the full training dataset.

翻译：从磁共振成像（MRI）和锥形束CT（CBCT）合成计算机断层扫描（CT）在自适应放射治疗中通过提供精确的解剖结构表征，对临床治疗规划起着关键作用。本文提出GANeXt，一种基于三维图像块、完全由ConvNeXt驱动的生成对抗网络，用于跨不同模态和解剖区域的统一CT合成。具体而言，GANeXt采用由堆叠的3D ConvNeXt块构建的高效U形生成器，这些块使用紧凑的卷积核；而判别器则采用条件式PatchGAN。为提升合成质量，我们结合了多种损失函数，包括平均绝对误差（MAE）、感知损失、基于分割的掩码MAE、对抗损失，以及用于多头分割判别器的Dice损失与交叉熵的组合。对于两项任务，训练批大小设为8，并分别为生成器和判别器使用两个独立的AdamW优化器，每个优化器均配备预热和余弦衰减调度器，学习率分别为$5\times10^{-4}$和$1\times10^{-3}$。数据预处理包括可变形配准、前景裁剪、输入模态的百分位归一化，以及将CT线性归一化至$[-1024, 1000]$范围。数据增强涉及在$(0.8, 1.3)$范围内随机缩放（仅用于MRI到CT任务）、固定尺寸裁剪（MRI到CT任务为$32\times160\times192$，CBCT到CT任务为$32\times128\times128$）以及随机翻转。在推理阶段，我们采用重叠率为$0.8$的滑动窗口方法并结合平均折叠来重建全尺寸合成CT，随后进行CT归一化的逆变换。在对所有区域进行联合训练且未进行任何微调后，最终模型在完整训练数据集上分别于第3000轮（MRI到CT）和第1000轮（CBCT到CT）结束时选定。