基于GAN的自动MRI体积合成从美国体积:一个概念验证调查的概念验证
Damjan Vukovic1,2, Igor Ruvinov3, Maria Antico4
1School of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, 2 George St, Brisbane, QLD, 4000, Australia. d.vukovic@qut.edu.au.
Scientific reports
|December 8, 2023
概括
这项研究引入了用于实时手术指导的深度学习方法,融合了超声波和MRI图像. 它使外科医生能够使用熟悉的参考成像来跟踪手术,改善胸部干预.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 基线MRI/CT扫描用于胸部手术前的参考,如胸腔切除术.
- 超声波 (美国) 指导针位置,但缺乏与参考方式的实时集成.
- 目前的方法缺乏实时指导和跟踪功能,使用熟悉的参考成像.
研究的目的:
- 为手术指导提出实时体积间接注册方法.
- 合并多种成像模式 (US和MRI) 以加强程序跟踪.
- 显示临床友好的参考成像模式 (MRI) 的实时变化.
主要方法:
- 利用使用CycleGAN,一种生成对抗网络 (GAN) 的深度学习方法.
- 执行无监督的图像对图像翻译,以创建空间对齐的US和MRI卷.
- 专注于T9脊椎进行初步的概念验证研究.
主要成果:
- 产生胸脊 (T9脊椎) 的空间对齐的US和MRI体积.
- 临床专家验证证实了解剖学准确性.
- 在子,重叠和T9脊椎的标签指标中获得了大约80%的准确性.
结论:
- 拟议的深度学习方法可以在胸部手术中实现实时指导和跟踪.
- 通过CycleGAN融合US和MRI,为改善外科导航提供了一个有希望的方法.
- 这种概念验证证明了加强程序监测和患者结果的潜力.
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