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跨扫描器超分辨率的3D电影MRI使用转移学习网络为MRgRT

Young Hun Yoon^1,2,3, Jaehee Chun⁴, Kendall Kiser³

¹Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.

Physics in medicine and biology

|April 25, 2024

概括

一个新的个性化超分辨率 (psSR) 网络提高了MR指导放射治疗的3D电影MRI质量. 这种深度学习方法克服了扫描器的变化,有效地提高了图像和细分的准确性.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
辐射疗法辐射疗法

背景情况:

深度学习超分辨率 (SR) 增强了用于MR指导放射治疗 (MRgRT) 的3DMRI空间分辨率.
扫描仪和患者之间的变化降低了实时3D低分辨率 (LR) 电影MRI中的SR质量.

研究的目的:

通过使用转移学习来呈现个性化的超分辨率 (psSR) 网络,以解决3D电影MRI中的跨扫描器SR挑战.
评估psSR对MRgRT应用的图像质量和临床可行性的影响.

主要方法:

开发了一个两阶段的网络:在1.5T时对53名患者进行队列特异性SR (csSR) 培训,然后在0.55T时使用转移学习对5名健康志愿者进行psSR.
使用峰值信号噪声比率 (PSNR) 和结构相似度指数 (SSIM) 测量评估的图像质量.
通过使用子相似系数 (DSC) 进行肝脏自分割来评估临床可行性.

主要成果:

与电影MRI相比,psSR显著增加了平均PSNR的57.2%和SSIM的94.7%.
使用psSR MRI,肝脏轮 DSC 改善了15%.
转移学习的平均时间为90秒,psSR重建时间为4.51ms/体积,自动细分时间为210ms.

结论:

该psSR网络在转移学习几分钟内大幅提高了3D电影MRI质量和细分精度.
这种方法有效地克服了深度学习对MRgRT的队列和扫描器依赖,改善了实时图像指导.

关键词:

3D电影磁力共振成像用MR指导的放射治疗.自动细分的自动细分.个性化网络个性化网络实时实时的时间.超级分辨率的超级分辨率

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