基于变压器的不同形状的腹部MRI-CT可变形图像注册
Yang Lei1, Luke A Matkovic1, Justin Roper1
1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Medical physics
|May 31, 2024
概括
一个新的深度学习模型准确地记录MRI和CT扫描用于肝脏立体体辐射疗法 (SBRT). 这种可变形图像注册 (DIR) 通过精确定义瘤体积和节省健康器官来改善治疗规划.
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:
- 立体体辐射疗法 (SBRT) 对肝转移至关重要,当手术不是一种选择时.
- 精确的成像 (CT和MRI) 对于精确的目标划分和最大限度地减少对有风险器官 (OAR) 的辐射毒性至关重要.
- 可变形图像注册 (DIR) 对于将基于MRI的轮转移到CT进行肝脏SBRT规划至关重要.
研究的目的:
- 开发一种新的深度学习模型,用于估计变形向量场 (DVF),用于直接MRI-CT在腹部成像中的注册.
- 为了提高图像注册的准确性,改善肝脏SBRT治疗计划.
主要方法:
- 一个包含Swin变压器和CNN的二次形态变形模型被用于DVF估计.
- 该模型使用跨模式图像相似性 (MIND) 和表面匹配损失进行了优化.
- 在50例肝脏病例中,使用目标注册错误 (TRE),子相似系数 (DSC) 和平均表面距离 (MSD) 来评估性能.
主要成果:
- 与刚性注册相比,拟议的DIR方法显著改善了肝脏 (0.850至0.903) 和门静脉 (0.628至0.763) 的平均DSC.
- 肝脏的平均表面距离 (MSD) 从7.216毫米减少到3.232毫米.
- 目标注册误差 (TRE) 从26.238mm减少到8.492mm.
结论:
- 开发的基于不同形变压器的DIR方法提供了一种有效和高效的方法,用于从腹部MRI-CT对准确生成DVF.
- 这种方法有可能被整合到目前的肝脏SBRT治疗规划工作流程中.
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