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骨架结构增强合成CT生成使用迪克逊序列的骨盆MR-only放射治疗

Xiao Liang¹, Allen Yen¹, Ti Bai¹

¹Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Medical physics

|June 26, 2023

概括

这项研究通过增强骨描绘来提高合成CT (sCT) 精度,用于仅MRI的放射治疗规划. 新方法使用了修改后的CycleGAN,具有骨结构约束,使MROP能够获得更可靠的电子密度信息.

科学领域:

医学物理医学物理
放射治疗技术放射治疗技术
人工智能在医学中的应用

背景情况:

仅用MRI进行放射治疗规划 (MROP) 与传统CT模拟相比,具有优势,包括减少辐射暴露和简化工作流程.
从MRI数据生成合成CT (sCT) 图像对于MROP至关重要,但像CycleGAN这样的无监督深度学习模型在准确的骨Hounsfield单位 (HU) 表示方面存在困难.
现有的无监督方法缺乏解剖学一致性,特别是在骨结构周围,限制了它们在MROP中的临床实用性.

研究的目的:

提高合成CT (sCT) 从MRI生成图像的准确性,特别是改善MRI仅用于放射治疗规划 (MROP) 的骨结构的描绘.
解决无监督深度学习模型在实现骨周围的解剖学一致性方面的局限性,以实现MR-to-sCT转换.

主要方法:

开发了一个修改后的多通道CycleGAN架构,将骨结构约束纳入损失函数,以指导生成过程.
用迪克森构建的脂肪和相位 (IP) 的MRI图像作为多通道输入被使用,与传统的T2加权图像相比,提供了优越的骨质对比.
该模型在一个私人数据集上进行了训练和测试,其中包括前列腺癌患者的31个扫描.

主要成果:

具有骨结构约束的多通道CycleGAN在骨和全身HU值 (分别为50.7和145.2HU) 中实现了最低的平均绝对误差 (MAE).
这种方法给出了与计划CT相比,骨结构的Dice相似度系数 (0.88) 是最高的.
对单通道和多通道输入的性能进行了评估,有或没有骨结构约束.

结论:

拟议的修改多通道CycleGAN,利用迪克森脂肪/IP图像和骨约束,有效生成临床上合适的sCT图像,具有准确的骨和软组织表示.
这些增强的sCT图像具有在MROP中精确剂量计算和患者定位的巨大潜力.
这一进步解决了MROP的一个关键挑战,为更广泛采用MRI-only工作流程铺平了道路.

关键词:

循环GANAN是一个循环.迪克森·迪克森 (Dixon Dixon) 是一个在这里,我们可以看到MR MR.骨头骨头骨头骨头合成CTCT 合成CT

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