Zhu Ruiming, Ruan Yuhui, Li Mingrui, Qian Wei, Yao Yudong, Teng Yueyang
College of Medicine and Biomedical Information Engineering, Northeastern University, 110169 Shenyang, People's Republic of China.
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ NJ07030, United States of America.
Phys Med Biol. 2025 Jun 17;70(12). doi: 10.1088/1361-6560/ade220.
. In radiotherapy planning, acquiring both magnetic resonance (MR) and computed tomography (CT) images is crucial for comprehensive evaluation and treatment. However, simultaneous acquisition of MR and CT images is time-consuming, economically expensive, and involves ionizing radiation, which poses health risks to patients. The objective of this study is to generate CT images from radiation-free MR images using a novel quasi-supervised learning framework.. In this work, we propose a quasi-supervised framework to explore the underlying relationship between unpaired MR and CT images. Normalized mutual information (NMI) is employed as a similarity metric to evaluate the correspondence between MR and CT scans. To establish optimal pairings, we compute an NMI matrix across the training set and apply the Hungarian algorithm for global matching. The resulting MR-CT pairs, along with their NMI scores, are treated as prior knowledge and integrated into the training process to guide the MR-to-CT image translation model.. Experimental results indicate that the proposed method significantly outperforms existing unsupervised image synthesis methods in terms of both image quality and consistency of image features during the MR to CT image conversion process. The generated CT images show a higher degree of accuracy and fidelity to the original MR images, ensuring better preservation of anatomical details and structural integrity.. This study proposes a quasi-supervised framework that converts unpaired MR and CT images into structurally consistent pseudo-pairs, providing informative priors to enhance cross-modality image synthesis. This strategy not only improves the accuracy and reliability of MR-CT conversion, but also reduces reliance on costly and scarce paired datasets. The proposed framework offers a practical and scalable solution for real-world medical imaging applications, where paired annotations are often unavailable.
在放射治疗计划中，获取磁共振（MR）图像和计算机断层扫描（CT）图像对于全面评估和治疗至关重要。然而，同时获取MR和CT图像既耗时又昂贵，并且涉及电离辐射，这会给患者带来健康风险。本研究的目的是使用一种新颖的准监督学习框架从无辐射的MR图像生成CT图像。在这项工作中，我们提出了一个准监督框架来探索未配对的MR和CT图像之间的潜在关系。归一化互信息（NMI）被用作相似性度量来评估MR和CT扫描之间的对应关系。为了建立最佳配对，我们在训练集上计算一个NMI矩阵，并应用匈牙利算法进行全局匹配。得到的MR-CT对及其NMI分数被视为先验知识，并整合到训练过程中以指导从MR到CT的图像转换模型。实验结果表明，在从MR到CT的图像转换过程中，所提出的方法在图像质量和图像特征一致性方面均显著优于现有的无监督图像合成方法。生成的CT图像对原始MR图像显示出更高的准确性和保真度，确保更好地保留解剖细节和结构完整性。本研究提出了一个准监督框架，该框架将未配对的MR和CT图像转换为结构一致的伪对，提供信息丰富的先验知识以增强跨模态图像合成。这种策略不仅提高了MR-CT转换的准确性和可靠性，还减少了对昂贵且稀缺的配对数据集的依赖。所提出的框架为现实世界的医学成像应用提供了一种实用且可扩展的解决方案，在这些应用中，配对注释通常不可用。
