在前列腺癌患者中使用治疗规划CT图像进行圆束CT图像的自动细分
Yoshiki Takayama1,2, Noriyuki Kadoya3, Takaya Yamamoto1
1Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan.
Radiological physics and technology
|August 14, 2025
概括
深度学习对束CT图像的细分通过使用混合里埃域适应方法来改进,以匹配图像质量. 这种新的方法提高了在线适应性放射治疗规划的准确性.
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:
- 基于形光束CT (CBCT) 的在线自适应性放射治疗 (ART) 对治疗精度至关重要.
- 由于图像质量变化,CBCT图像的深度学习细分存在重大挑战.
- 目前用于生成CBCT细分数据集的现有方法存在局限性.
研究的目的:
- 增强基于深度学习的CBCT图像对男性骨盆放射治疗的细分.
- 提高CBCT图像质量,使其与治疗计划CT (tpCT) 相匹配,而不会改变原始数据.
- 为在线适应性放射治疗开发一个强大的细分模型.
主要方法:
- 一个深度学习模型被训练在一个男性骨盆 tpCT 数据集上.
- 使用高斯噪声和富里埃域适应 (FDA) (混合FDA方法) 生成了一个人工伪CBCT数据集.
- 混合FDA方法应用于80个tpCT数据集,以弥合tpCT和CBCT图像之间的质量差距.
主要成果:
- 混合FDA方法显著提高了细分精度,相比于在平面tpCT上训练的模型或使用以图谱为基础的细分的模型.
- 使用混合FDA方法,临床目标体积,膀和直肠的平均子相似系数 (DSC) 分别为0.71,0.84和0.78.
- 在平面tpCT上训练的模型产生了0.40,0.17和0.18的DSC值,而基于图谱的方法实现了0.66,0.59和0.66.
结论:
- 混合FDA方法有效地提高了CBCT图像质量,用于深度学习细分.
- 这种方法为线上适应性放射治疗的细分精度提供了显著的改善.
- 开发的模型有望提高放射治疗规划的效率和有效性.
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