Magnetic resonance (MR) and computer tomography (CT) imaging are valuable tools for diagnosing diseases and planning treatment. However, limitations such as radiation exposure and cost can restrict access to certain imaging modalities. To address this issue, medical image synthesis can generate one modality from another, but many existing models struggle with high-quality image synthesis when multiple slices are present in the dataset. This study proposes an attention-based dual contrast generative model, called ADC-cycleGAN, which can synthesize medical images from unpaired data with multiple slices. The model integrates a dual contrast loss term with the CycleGAN loss to ensure that the synthesized images are distinguishable from the source domain. Additionally, an attention mechanism is incorporated into the generators to extract informative features from both channel and spatial domains. To improve performance when dealing with multiple slices, the $K$-means algorithm is used to cluster the dataset into $K$ groups, and each group is used to train a separate ADC-cycleGAN. Experimental results demonstrate that the proposed ADC-cycleGAN model produces comparable samples to other state-of-the-art generative models, achieving the highest PSNR and SSIM values of 19.04385 and 0.68551, respectively. We publish the code at https://github.com/JiayuanWang-JW/ADC-cycleGAN.

翻译：磁共振（MR）和计算机断层扫描（CT）成像是诊断疾病和制定治疗计划的重要工具。然而，辐射暴露和成本等限制因素可能制约某些成像模态的可及性。为解决这一问题，医学图像合成可通过一种模态生成另一种模态，但现有许多模型在处理数据集中存在多个切片的高质量图像合成时面临挑战。本研究提出一种基于注意力的双对比生成模型，称为ADC-cycleGAN，该模型能够从包含多个切片的非配对数据中合成医学图像。该模型将双对比损失项与CycleGAN损失相结合，以确保合成图像与源域图像可区分。此外，在生成器中引入注意力机制，从通道域和空间域中提取信息性特征。为提升处理多个切片时的性能，采用$K$-均值算法将数据集聚类为$K$组，每组用于训练独立的ADC-cycleGAN。实验结果表明，所提出的ADC-cycleGAN模型生成的样本与其他先进生成模型相当，其峰值信噪比（PSNR）和结构相似性指数（SSIM）最高分别达到19.04385和0.68551。我们将代码公开发布于https://github.com/JiayuanWang-JW/ADC-cycleGAN。