从MRI使用生成对抗网络进行合成CT生成,用于MR-only脑放射治疗规划
Chengjian Xiao1, Chaozhe Cen2, Qun Huang3
1Department of Radiation Oncology, Ganzhou Cancer Hospital, Ganzhou, People's Republic of China.
Technology in cancer research & treatment
|December 1, 2025
概括
这项研究引入了一种新的CBAMPix2Pix模型,用于从MRI扫描中生成合成CT (sCT) 图像,这对于放射治疗至关重要. 该模型通过从MRI数据中生成现实的CT图像来提高治疗规划的准确性.
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:
- 磁共振成像 (MRI) 在放射治疗中对于精确的目标划分至关重要.
- 从MRI生成合成CT (sCT) 具有挑战性,因为缺少电子密度信息,限制了临床使用.
- 准确的sCT对于基于MRI的放射治疗治疗计划至关重要.
研究的目的:
- 开发和评估一种新的CBAMPix2Pix模型,用于合成来自MRI的CT图像.
- 提高基于MRI的放射治疗的准确性和临床适用性.
- 为应对MRI到CT合成中电子密度信息的挑战.
主要方法:
- 调整了一个CBAMPix2Pix架构,包含一个卷积块注意模块 (CBAM).
- 训练了该模型对来自86名转移性脑癌患者的17260张MRI片进行了训练,将T1加权对比增强 (T1wc) MRI映射到sCT.
- 引入结构相似性损失 (SSIM) 以增强局部特征并减少Hounsfield单位 (HU) 的差异,解决正常组织和骨之间的数据不平衡.
主要成果:
- 在CBAMPix2Pix模型中,PSNR达到27.5±3.3dB,NMAE达到0.019±0.023,SSIM达到0.857±0.059.
- 平均绝对误差 (MAE) 为体内组织的74.48 ± 22.88 HU,骨的185.89 ± 21.59 HU.
- 统计分析 (Wilcoxon签名等级测试) 显示PSNR,SSIM,MAE和NMAE的其他模型相比显著改善 (P <0.05).
结论:
- 一个新的CBAMPix2Pix模型有效地从MRI中生成现实的sCT图像.
- 合成的sCT图像与真实CT图像可比,提高了基于MRI的治疗规划准确性.
- 这一进步有可能提高放射治疗的准确性和MRI数据的临床应用.
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