Wang Tonghe, Manohar Nivedh, Lei Yang, Dhabaan Anees, Shu Hui-Kuo, Liu Tian, Curran Walter J, Yang Xiaofeng
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
Med Dosim. 2019;44(3):199-204. doi: 10.1016/j.meddos.2018.06.008. Epub 2018 Aug 14.
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast without ionizing radiation compared with computed tomography (CT). However, it requires the generation of pseudo CT from MRI images for patient setup and dose calculation. Our machine-learning-based method to generate pseudo CT images has been shown to provide pseudo CT images with excellent image quality, while its dose calculation accuracy remains an open question. In this study, we aim to investigate the accuracy of dose calculation in brain frameless stereotactic radiosurgery (SRS) using pseudo CT images which are generated from MRI images using the machine learning-based method developed by our group. We retrospectively investigated a total of 19 treatment plans from 14 patients, each of whom has CT simulation and MRI images acquired during pretreatment. The dose distributions of the same treatment plans were calculated on original CT simulation images as ground truth, as well as on pseudo CT images generated from MRI images. Clinically-relevant DVH metrics and gamma analysis were extracted from both ground truth and pseudo CT results for comparison and evaluation. The side-by-side comparisons on image quality and dose distributions demonstrated very good agreement of image contrast and calculated dose between pseudo CT and original CT. The average differences in Dose-volume histogram (DVH) metrics for Planning target volume (PTVs) were less than 0.6%, and no differences in those for organs at risk at a significance level of 0.05. The average pass rate of gamma analysis was 99%. These quantitative results strongly indicate that the pseudo CT images created from MRI images using our proposed machine learning method are accurate enough to replace current CT simulation images for dose calculation in brain SRS treatment. This study also demonstrates the great potential for MRI to completely replace CT scans in the process of simulation and treatment planning.
仅使用磁共振成像（MRI）的放射治疗治疗计划很有吸引力，因为与计算机断层扫描（CT）相比，MRI能提供出色的软组织对比度且无电离辐射。然而，它需要从MRI图像生成伪CT用于患者定位和剂量计算。我们基于机器学习的生成伪CT图像的方法已被证明能提供具有出色图像质量的伪CT图像，但其剂量计算准确性仍是一个悬而未决的问题。在本研究中，我们旨在研究在脑立体定向放射外科（SRS）中使用通过我们团队开发的基于机器学习方法从MRI图像生成的伪CT图像进行剂量计算的准确性。我们回顾性研究了来自14名患者的总共19个治疗计划，每位患者在治疗前均获取了CT模拟图像和MRI图像。在原始CT模拟图像上计算相同治疗计划的剂量分布作为参考标准，同时也在从MRI图像生成的伪CT图像上进行计算。从参考标准和伪CT结果中提取临床相关的剂量体积直方图（DVH）指标和伽马分析结果进行比较和评估。对图像质量和剂量分布的并排比较表明，伪CT和原始CT之间在图像对比度和计算剂量方面具有非常好的一致性。计划靶体积（PTV）的DVH指标平均差异小于0.6%，在0.05的显著性水平下，危及器官的指标无差异。伽马分析的平均通过率为99%。这些定量结果有力地表明，使用我们提出的机器学习方法从MRI图像创建的伪CT图像足够准确，可替代当前的CT模拟图像用于脑SRS治疗中的剂量计算。本研究还证明了MRI在模拟和治疗计划过程中完全取代CT扫描的巨大潜力。
