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从CBCT和MRI使用StarGAN在骨盆区域生成合成CT

Paritt Wongtrakool¹, Chanon Puttanawarut^2,3, Pimolpun Changkaew⁴

¹Master of Science Program in Medical Physics, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.

Radiation oncology (London, England)

|February 5, 2025

概括

StarGAN从MRI和CBCT生成合成CT图像,显示更好的放射治疗的解剖细节. 虽然CycleGAN的误差较低,但StarGAN为改善MRI模拟和适应性辐射疗法提供了潜力.

科学领域:

医疗成像医学成像
人工智能的人工智能
辐射疗法辐射疗法

背景情况:

在放射治疗中,精确的剂量计算需要计算机断层扫描 (CT) 数据.
磁共振成像 (MRI) 和束CT (CBCT) 在放射治疗中越来越多地使用,但缺乏直接CTHounsfield单位 (HU) 信息.
合成CT (sCT) 生成旨在弥合这一差距,使MRI/CBCT的剂量计算成为可能.

研究的目的:

评估StarGAN,一种用于从MRI和CBCT数据生成sCT的深度学习模型.
为了将StarGAN的性能与sCT生成的CycleGAN进行比较.
评估用于放射治疗应用的sCT图像的剂量测量精度和解剖性保存.

主要方法:

使用StarGAN和CycleGAN模型进行sCT生成.
数据集包括53例盆腔癌病例.
使用定性和定量分析评估sCT质量,包括剂量分配计算.

主要成果:

在CBCT和MRI产生的sCT中,StarGAN表现出优异的解剖学保存.
对于身体和骨的HU值,CycleGAN显示了较低的平均绝对误差 (MAE).
两种模型都实现了可接受的剂量测量准确性 (平均剂量差<2%,玛通道率>90%).

结论:

在SCT生成方面,StarGAN在解剖学准确度方面表现出色,这对于放射治疗至关重要.
尽管有较高的定量错误,但StarGAN的解剖学真实性为MRI模拟和适应性辐射疗法提供了显著的潜力.
在StarGAN和CycleGAN之间做出选择可能取决于对定量准确性和解剖细节的具体要求.

关键词:

在CBCT中,CBCT是CBCT.循环GANAN是一个循环.深度学习是一种深度学习.这就是为什么MRI是MRI.辐射疗法辐射疗法星光公司 (StarGAN)合成CTCT是一种合成的CTCT.

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