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用对比增强的肝脏磁共振图像合成使用梯度规范的多模多区别的稀疏注意力融合GAN

Changzhe Jiao^1,2, Diane Ling¹, Shelly Bian¹

¹Department of Radiation Oncology, Keck School of Medicine of USC, Los Angeles, CA 90033, USA.

|July 29, 2023

概括

这项研究介绍了GRMM-GAN,这是一种新的AI模型,用于从前对比图像中合成对比增强的MRI扫描. 这种方法旨在减少患者的对比注射,并改善肝癌治疗中的适应性监测.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

增强对比度的MRI对于肝癌监测至关重要,但需要重复注射.
开发用于合成对比增强图像的非侵入性方法至关重要.

研究的目的:

开发一个生成对抗网络 (GRMM-GAN) 来从T1和T2前对比图像中合成对比增强的T1 (T1ce) MR图像.
在接受腹部MRI的患者中减少对比剂重复注射的需要.

主要方法:

一个渐变规范化的多模式多歧视稀疏注意力融合生成对抗网络 (GRMM-GAN) 被开发出来.
该模型在来自61名肝癌患者的165个腹部MR研究中进行了训练和测试.
使用PSNR,SSIM,MSE,图灵测试和专家轮分析来评估图像合成质量.

主要成果:

在最先进的模型中,GRMM-GAN实现了优越的性能,PSNR为28.56,SSIM为0.869,MSE为83.27.
图灵测试结果表明,合成图像几乎无法与真实图像区分 (52.33%的得分).
与地面真相相比,瘤对比度和DICE得分 (0.90) 显示了合成图像的高保真性.

结论:

新型GRMM-GAN有效地从前对比T1和T2图像中合成T1ceMR图像.
这种人工智能驱动的方法显示出临床应用的巨大潜力,特别是避免在放射治疗期间重复注射对比剂.

关键词:

没有了,没有了,没有了.合成MR的合成方法对比度增强剂增强对比度的增强剂多模式融合融合多模式融合瘤监测:瘤监测是指对瘤进行监测.

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