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MRI-based treatment plan simulation and adaptation for ion radiotherapy using a classification-based approach

机译:基于分类的方法基于MRI的治疗计划模拟和离子放射疗法的适应性

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Background In order to benefit from the highly conformal irradiation of tumors in ion radiotherapy, sophisticated treatment planning and simulation are required. The purpose of this study was to investigate the potential of MRI for ion radiotherapy treatment plan simulation and adaptation using a classification-based approach. Methods Firstly, a voxelwise tissue classification was applied to derive pseudo CT numbers from MR images using up to 8 contrasts. Appropriate MR sequences and parameters were evaluated in cross-validation studies of three phantoms. Secondly, ion radiotherapy treatment plans were optimized using both MRI-based pseudo CT and reference CT and recalculated on reference CT. Finally, a target shift was simulated and a treatment plan adapted to the shift was optimized on a pseudo CT and compared to reference CT optimizations without plan adaptation. Results The derivation of pseudo CT values led to mean absolute errors in the range of 81 - 95 HU. Most significant deviations appeared at borders between air and different tissue classes and originated from partial volume effects. Simulations of ion radiotherapy treatment plans using pseudo CT for optimization revealed only small underdosages in distal regions of a target volume with deviations of the mean dose of PTV between 1.4 - 3.1% compared to reference CT optimizations. A plan adapted to the target volume shift and optimized on the pseudo CT exhibited a comparable target dose coverage as a non-adapted plan optimized on a reference CT. Conclusions We were able to show that a MRI-based derivation of pseudo CT values using a purely statistical classification approach is feasible although no physical relationship exists. Large errors appeared at compact bone classes and came from an imperfect distinction of bones and other tissue types in MRI. In simulations of treatment plans, it was demonstrated that these deviations are comparable to uncertainties of a target volume shift of 2 mm in two directions indicating that especially applications for adaptive ion radiotherapy are interesting.
机译:背景技术为了从离子放射疗法中高度保形地照射肿瘤中受益,需要复杂的治疗计划和模拟。这项研究的目的是研究使用基于分类的方法进行核磁共振成像在离子放射治疗计划模拟和适应方面的潜力。方法首先,使用体素组织分类法使用多达8个对比从MR图像中得出伪CT编号。在对三个体模的交叉验证研究中评估了合适的MR序列和参数。其次,使用基于MRI的伪CT和参考CT来优化离子放射治疗计划,并在参考CT上重新计算。最后,对目标班次进行了仿真,并在伪CT上优化了适合该班次的治疗计划,并将其与没有计划修改的参考CT优化进行了比较。结果伪CT值的推导导致平均绝对误差在81-95 HU范围内。最显着的偏差出现在空气和不同组织类别之间的边界,并且是由部分体积效应引起的。使用伪CT进行优化的离子放射治疗计划的模拟显示,目标体积的远端区域仅存在少量剂量不足,与参考CT优化相比,PTV的平均剂量偏差在1.4-3.1%之间。适应目标体积偏移并在伪CT上优化的计划与未参考CT上优化的非适应计划相比,具有可比的目标剂量覆盖率。结论我们能够证明,尽管不存在物理关系,但使用纯统计分类方法基于MRI的伪CT值的推导是可行的。大的错误出现在紧凑的骨骼类别上,并且是由于MRI中骨骼和其他组织类型的不完美区分所致。在治疗计划的模拟中,已证明这些偏差可与两个方向上2 mm的目标体积偏移的不确定性相媲美,这表明自适应离子放射疗法的应用尤为有趣。

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