MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method.
MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method.
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DOI：
10.1016/j.meddos.2018.06.008
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Yang X
中科院分区：
文献类型：
--
作者：
Wang T;Manohar N;Lei Y;Dhabaan A;Shu HK;Liu T;Curran WJ;Yang X
磁共振成像(MRI)是唯一有吸引力的放射治疗计划，因为与计算机断层扫描(CT)相比，MRI提供了无电离辐射的优越软组织对比度。然而，它需要从MRI图像生成伪CT，用于患者设置和剂量计算。我们的基于机器学习的伪CT图像生成方法已经被证明提供了具有良好图像质量的伪CT图像，但其剂量计算的准确性仍然是一个有待解决的问题。在这项研究中，我们的目的是研究在脑无框架立体定向放射外科(SRS)中使用伪CT图像计算剂量的准确性。伪CT图像是由我们团队开发的基于机器学习的方法从MRI图像生成的。我们对14名患者的19个治疗计划进行了回顾性调查，每个治疗计划都有在治疗前获得的CT模拟和MRI图像。根据原始CT模拟图像和由MRI图像生成的伪CT图像，计算相同治疗计划的剂量分布。临床上相关的DVH指标和伽马分析都是从地面真实和伪CT结果中提取出来的，用于比较和评估。图像质量和剂量分布的对比表明，伪CT和原始CT的图像对比度和计算的剂量非常吻合。计划靶体积(PTVS)的剂量-体积直方图(DVH)指标的平均差异小于0.6%，危险器官的DVH指标差异无统计学意义(P>0.05)。伽马分析平均合格率为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图像生成的伪CT图像足够准确，可以代替当前的CT模拟图像用于脑SRS治疗中的剂量计算。这项研究也证明了MRI在模拟和治疗计划过程中完全取代CT扫描的巨大潜力。
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.
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