走向基于深度学习的磁共振成像仅用于I-125种子胸膜治疗前列腺癌的植入后剂量计工作流
Johanna Grigo1, Andre Karius1, Jannis Hanspach2
1Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Brachytherapy
|November 26, 2023
概括
一种使用MRI扫描的新型深度学习方法,在前列腺骨干治疗后准确地定位种子. 这种磁共振成像 (MRI) 唯一方法为植入后剂量测量CT扫描提供了一个有希望的替代方案.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 辐射瘤学 辐射瘤学
背景情况:
- 计算机断层扫描 (CT) 是前列腺骨干治疗后期计划的标准,但涉及辐射和软组织对比度差.
- 磁共振成像 (MRI) 提供卓越的软组织可视化,避免额外的辐射暴露.
研究的目的:
- 开发和评估深度学习 (DL) 方法,用于MRI扫描上的自动种子细分.
- 建立一个MRI-only工作流程,用于前列腺骨干疗法后期规划.
主要方法:
- 使用DIXON和从20名患者的1.5TMRI扫描中的定量敏感度图 (QSM) 训练了一种3DnnU-net模型.
- 基于CT的种子细分是培训和验证的基本真理.
- 使用QSM,DIXON和组合数据评估了DL模型.
主要成果:
- 在使用QSM和DIXON数据进行训练时,DL模型实现了94.8%的种子检测.
- 个人QSM或DIXON培训导致较低的检测率 (分别为87.5%和88.6%).
- 种子定位的准确性很高,植入后剂量测量有很小的变化 (D90至0.4±4.26 Gy).
结论:
- 拟议的DL-based MRI-only工作流程证明了精确而强大的种子定位.
- 这种方法有可能在前列腺支架治疗中取代基于CT的剂量计.
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