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3D无监督深度学习方法用于前列腺放射治疗中的磁共振成像到计算机断层扫描合成

Blanche Texier¹, Cédric Hémon¹, Adélie Queffélec¹

¹Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.

Physics and imaging in radiation oncology

|August 20, 2024

概括

无监督的深度学习精确地合成了前列腺放射治疗的MRI数据中的CT图像,消除了注册需求,并确保了多个中心的精确剂量计算. 这使得只有MRI的工作流程取得了进展.

科学领域:

医疗成像医学成像
放射治疗物理物理
人工智能在医学中的应用

背景情况:

从MRI获得准确的合成CT (sCT) 生成对于仅MRI的放射治疗工作流程至关重要.
对sCT生成的监督深度学习方法受到中心特定数据和注册精度的限制.
无监督学习为克服这些局限性提供了一个潜在的解决方案.

研究的目的:

评估前列腺MRI-to-CT合成无监督和监督方法的准确性.
评估合成CT (sCT) 图像在放射治疗剂量计算中的剂量影响.
调查使用多中心数据进行可靠的sCT生成的可行性.

主要方法:

对监督和无监督的条件生成对抗网络 (cGAN) 的比较使用CT/MRI图像对从三个中心的99名前列腺癌患者进行.
无监督培训包括一种风格转移方法,内容和风格表现用于增强感知合成 (CREPs) 的损失.
放射治疗剂量计算在VMAT中使用60Gy的光子处方剂量进行评估,用于sCT成像的平均绝对误差 (MAE) 和用于剂量测量终点的马分析.

主要成果:

无监督配对网络实现了身体合成的最高精度,MAE为33.6 HU.
最高的MAE是45.5HU,没有监督的无对学习.
所有测试的架构都产生了临床上可接受的剂量计算,马传递率超过94% (1% / 1mm).

结论:

多中心数据通过无监督学习实现了准确的sCT生成,消除了CT-MRI注册的需要.
合成的CT图像准确地反映了HU值,并支持精确的放射治疗剂量计算.
无监督的sCT生成显示出在MRI-only放射治疗工作流程中广泛采用的巨大潜力.

关键词:

感知损失是一种感知损失.合成CTCT是一种合成的CTCT.没有监督的学习学习.在CHANGAN中,你会看到.

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