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基于未配对数据的准监督MR-CT图像转换

Ruiming Zhu¹, Yuhui Ruan¹, Mingrui Li¹

¹College of Medicine and Biomedical Information Engineering, Northeastern University, 110169 Shenyang, People's Republic of China.

Physics in medicine and biology

|June 6, 2025

概括

这项研究引入了一种准监督学习框架,用于从MRI扫描中生成CT图像,从而减少辐射暴露和放射治疗规划中的成本. 这种新的方法提高了图像质量和解剖学准确性,以更好地治疗患者.

科学领域:

医疗成像医学成像
医疗保健中的人工智能
放射治疗规划放射治疗规划

背景情况:

同时获取MRI和CT图像对于放射治疗计划至关重要,但成本昂贵,耗时,并且涉及电离辐射.
从MRI生成CT图像可以减轻这些问题,但需要精确的交叉模式图像合成.

研究的目的:

从无辐射MR图像生成CT图像的新型准监督学习框架的开发.
为了提高MR-to-CT图像翻译的准确性和真实性,用于放射治疗应用.

主要方法:

开发了一个准监督的框架来分析未配对的MR和CT图像.
标准化相互信息 (NMI) 和匈牙利算法被用来建立最佳的MR-CT配对.
这些对和NMI分数作为先验指导MR-to-CT图像转换模型.

主要成果:

拟议的方法在图像质量和特征一致性方面明显优于现有的无监督方法.
生成的CT图像显示出更高的准确性和真实性,保留了解剖细节和结构完整性.
该框架成功地将未配对的MR和CT图像转换为结构一致的伪对.

结论:

准监督框架提高了MR-to-CT转换的准确性和可靠性.
这种方法减少了对昂贵和稀缺的医疗成像数据集的依赖.
它为缺乏配对注释的医学成像应用提供了实用且可扩展的解决方案.

关键词:

图像转换MR-CT图像转换深度学习是一种深度学习.准监督的学习学习.没有配对的数据.

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