自动CorNN:一个无监督的物理意识深度学习模型,用于对脑MRI图像进行几何扭曲校正,朝着MR-only立体射线外科手术
Mahboube Sadat Hosseini1, Seyed Mahmoud Reza Aghamiri2, Ali Fatemi Ardekani3,4
1Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, 1983969411, Iran.
Journal of imaging informatics in medicine
|July 30, 2024
概括
AutoCorNN是一种新的深度学习模型,可以在不需要地面真相数据的情况下纠正脑部MRI扫描中的几何扭曲. 这一进步提高了高精度放射治疗的准确性,提高了治疗结果.
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:
- 大脑MRI中的几何扭曲,由空气-组织接口的敏感性人工物引起,阻碍了立体射线外科手术所需的亚毫米精度.
- 准确的成像对于精确的辐射治疗规划和实施至关重要.
研究的目的:
- 开发和评估AutoCorNN,一个无监督的,物理意识的深度学习模型,用于纠正脑部MRI中的几何扭曲.
- 评估扭曲校正对辐射治疗中目标体积,覆盖范围和剂量合规性的影响.
主要方法:
- 开发了AutoCorNN,将两个2D卷积编码器-解码器网络与MRI信号生成的前向物理模型集成在一起.
- 通过最大限度地减少k空间数据错误,以无监督的方式使用两个大型公共数据集 (MPI-莱比锡思维-大脑-身体和静脉瘤-SEG) 训练模型.
- 评估了前置神经瘤病例的模型,并将其性能与未经纠正和B0映射方法进行比较.
主要成果:
- 自动CorNN实现了高图像质量指标:PSNR为41.35 ± 0.02dB,RMSE为0.02 ± 0.003和SSIM为0.99 ± 0.02.
- 扭曲校正减少了空气-组织接口 (如鼻腔) 的几何错误,并将目标体积从3.12升至3.84厘米升.
- 在剂量测量方面,AutoCorNN提高了目标覆盖率 (0.96至0.97) 和合格指数 (0.92至0.94),同时减少了不必要的剂量梯度.
结论:
- AutoCorNN有效地纠正几何MRI扭曲,具有高精度和计算效率.
- 该模型允许精确的目标划分和合规剂量递送,显著改善放射治疗的结果.
- 这种无监督的,以物理为基础的方法为提高图像引导放射治疗的精度提供了有希望的解决方案.
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