Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America.
Phys Med Biol. 2020 Dec 4;65(24). doi: 10.1088/1361-6560/ab9fcb.
Adaptive proton therapy (APT) is a promising approach for the treatment of head and neck cancers. One crucial element of APT is daily volumetric imaging of the patient in the treatment position. Such data can be acquired with cone-beam computed tomography (CBCT), although scatter artifacts make uncorrected CBCT images unsuitable for proton therapy dose calculation. The purpose of this work is to evaluate the performance of a U-shape deep convolutive neural network (U-Net) to perform projection-based scatter correction and enable fast and accurate dose calculation on CBCT images in the context of head and neck APT. CBCT projections are simulated for a cohort of 48 head and neck patients using a GPU accelerated Monte Carlo (MC) code . A U-Net is trained to reproduce MC projection-based scatter correction from raw projections. The accuracy of the scatter correction is experimentally evaluated using CT and CBCT images of an anthropomorphic head phantom. The potential of the method for head and neck APT is assessed by comparing proton therapy dose distributions calculated on scatter-free, uncorrected and scatter-corrected CBCT images. Finally, dose calculation accuracy is estimated in experimental patient images using a previously validated empirical scatter correction as reference. The mean and mean absolute HU differences between scatter-free and scatter-corrected images are -0.8 and 13.4 HU, compared to -28.6 and 69.6 HU for the uncorrected images. In the head phantom, the root-mean square difference of proton ranges calculated in the reference CT and corrected CBCT is 0.73 mm. The average 2%/2 mm gamma pass rate for proton therapy plans optimized in the scatter free images and re-calculated in the scatter-corrected ones is 98.89%. In experimental CBCT patient images, a 3%/3 mm passing rate of 98.72% is achieved between the proposed method and the reference one. All CBCT projection volume could be corrected in less than 5 seconds.
自适应质子治疗（APT）是治疗头颈部癌症的一种很有前途的方法。APT 的一个关键要素是在治疗位置对患者进行每日容积成像。这种数据可以使用锥形束计算机断层扫描（CBCT）获得，尽管散射伪影使得未经校正的 CBCT 图像不适合质子治疗剂量计算。这项工作的目的是评估 U 形深度卷积神经网络（U-Net）在头颈部 APT 中执行基于投影的散射校正并能够快速准确地计算 CBCT 图像上的剂量的性能。使用 GPU 加速的蒙特卡罗（MC）代码模拟了 48 例头颈部患者的 CBCT 投影。U-Net 经过训练可从原始投影中再现基于 MC 投影的散射校正。使用人体头部模型的 CT 和 CBCT 图像实验评估散射校正的准确性。通过比较无散射、未校正和散射校正的 CBCT 图像上计算的质子治疗剂量分布来评估该方法对头颈部 APT 的潜力。最后，使用先前验证的经验散射校正作为参考，在实验患者图像中估计剂量计算准确性。与未校正图像相比，无散射和校正图像之间的平均和平均绝对 HU 差异分别为 -0.8 和 13.4 HU，而未校正图像分别为 -28.6 和 69.6 HU。在头部模型中，在参考 CT 和校正 CBCT 中计算的质子射程的均方根差为 0.73 毫米。在无散射图像中优化并在散射校正图像中重新计算的质子治疗计划的平均 2%/2mm 伽马通过率为 98.89%。在实验性 CBCT 患者图像中，该方法与参考方法之间的 3%/3mm 通过率为 98.72%。所有 CBCT 投影体积都可以在不到 5 秒的时间内校正。
