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Stability of conventional and machine learning‐based tumor auto‐segmentation techniques using undersampled dynamic radial bSSFP acquisitions on a 0.35 T hybrid MR‐linac system
Medical Physics ( IF 3.8 ) Pub Date : 2020-12-15 , DOI: 10.1002/mp.14659
Florian Friedrich 1, 2 , Juliane Hörner‐Rieber 3, 4, 5, 6 , C. Katharina Renkamp 3, 4, 5 , Sebastian Klüter 3, 4, 5 , Peter Bachert 1, 2 , Mark E. Ladd 1, 2, 7 , Benjamin R. Knowles 1
Affiliation  

Hybrid MRI‐linear accelerator systems (MR‐linacs) allow for the incorporation of MR images with high soft‐tissue contrast into the radiation therapy procedure prior to, during, or post irradiation. This allows not only for the optimization of the treatment planning, but also for real‐time monitoring of the tumor position using cine MRI, from which intrafractional motion can be compensated. Fast imaging and accurate tumor tracking are crucial for effective compensation. This study investigates the application of cine MRI with a radial acquisition scheme on a low‐field MR‐linac to accelerate the acquisition rate and evaluates the effect on tracking accuracy.

中文翻译:

在0.35 T混合MR-直线加速器系统上使用欠采样动态径向bSSFP采集的常规和基于机器学习的肿瘤自动分割技术的稳定性

混合MRI线性加速器系统(MR-linacs)允许在照射之前,期间或之后将具有高软组织对比度的MR图像合并到放射治疗程序中。这不仅可以优化治疗计划,还可以使用电影MRI实时监测肿瘤位置,从而可以补偿分数内运动。快速成像和准确的肿瘤追踪对于有效的补偿至关重要。这项研究调查了电影MRI在低场MR直线加速器上的径向采集方案的应用,以加快采集速率,并评估其对跟踪精度的影响。
更新日期:2021-02-22
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