GANeXt: 一种完全基于ConvNeXt增强的生成对抗网络用于MRI和CBCT到CT的合成
GANeXt: A Fully ConvNeXt-Enhanced Generative Adversarial Network for MRI- and CBCT-to-CT Synthesis
AI总结：
GANeXt通过ConvNeXt增强的生成对抗网络实现MRI和CBCT到CT的跨模态合成，结合多种损失函数提升合成质量。
摘要：
从磁共振成像（MRI）和锥束CT（CBCT）合成计算断层扫描（CT）在临床治疗计划中起着关键作用，因为它能够为自适应放疗提供精确的解剖表示。在本工作中，我们提出GANeXt，一种基于3D补丁的、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采用了一个高效的U型生成器，由堆叠的3D ConvNeXt块构建而成，使用紧凑的卷积核；而判别器则采用条件性PatchGAN。为了提高合成质量，我们结合了多种损失函数，包括均方误差（MAE）、感知损失、基于分割的掩码MAE以及对抗损失，以及用于多头分割判别器的Dice损失和交叉熵损失。对于两种任务，训练使用批大小为8，分别使用两个独立的AdamW优化器进行训练，每个优化器都配备了warmup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CT的线性归一化到范围$[-1024, 1000]$。数据增强包括在$(0.8, 1.3)$范围内随机缩放（仅适用于MRI到CT）、固定大小裁剪到$32\times160\times192$（MRI到CT）和$32\times128\times128$（CBCT到CT），以及随机翻转。在推理过程中，我们应用滑动窗口方法，重叠率为$0.8$，并使用平均折叠来重建完整的sCT，随后对CT归一化进行反向操作。在没有任何微调的情况下，对所有区域进行联合训练后，最终模型在MRI到CT训练过程中在3000个epoch结束时选择，CBCT到CT训练过程中在1000个epoch结束时选择。
英文摘要：
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.
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。
