Lapaeva Mariia, La Greca Saint-Esteven Agustina, Wallimann Philipp, Günther Manuel, Konukoglu Ender, Andratschke Nicolaus, Guckenberger Matthias, Tanadini-Lang Stephanie, Dal Bello Riccardo
Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Artificial Intelligence and Machine Learning Group, Department of Informatics, University of Zurich, Zurich, Switzerland.
Phys Imaging Radiat Oncol. 2022 Nov 28;24:173-179. doi: 10.1016/j.phro.2022.11.011. eCollection 2022 Oct.
The requirement of computed tomography (CT) for radiotherapy planning may be bypassed by synthetic CT (sCT) generated from magnetic resonance (MR), which has recently led to the clinical introduction of MR-only radiotherapy for specific sites. Further developments are required for abdominal sCT, mostly due to the presence of mobile air pockets affecting the dose calculation. In this study we aimed to overcome this limitation for abdominal sCT at a low field (0.35 T) hybrid MR-Linac.
A retrospective analysis was conducted enrolling 168 patients corresponding to 215 MR-CT pairs. After the exclusion criteria, 152 volumetric images were used to train the cycle-consistent generative adversarial network (CycleGAN) and 34 to test the sCT. Image similarity metrics and dose recalculation analysis were performed.
The generated sCT faithfully reproduced the original CT and the location of the air pockets agreed with the MR scan. The dose calculation did not require manual bulk density overrides and the mean deviations of the dose-volume histogram dosimetric points were within 1 % of the CT, without any outlier above 2 %. The mean gamma passing rates were above 99 % for the 2 %/ 2 mm analysis and no cases below 95 % were observed.
This study presented the implementation of CycleGAN to perform sCT generation in the abdominal region for a low field hybrid MR-Linac. The sCT was shown to correctly allocate the electron density for the mobile air pockets and the dosimetric analysis demonstrated the potential for future implementation of MR-only radiotherapy in the abdomen.
磁共振成像（MR）生成的合成CT（sCT）可替代计算机断层扫描（CT）用于放射治疗计划，这促使特定部位仅使用MR的放射治疗技术在临床上得到应用。腹部sCT仍需进一步改进，主要是因为存在影响剂量计算的移动气腔。本研究旨在克服低场（0.35 T）混合MR直线加速器腹部sCT的这一局限性。
进行回顾性分析，纳入168例患者的215对MR-CT图像。根据排除标准，152幅容积图像用于训练循环一致生成对抗网络（CycleGAN），34幅用于测试sCT。进行了图像相似性指标和剂量重新计算分析。
生成的sCT忠实地再现了原始CT，气腔位置与MR扫描一致。剂量计算无需手动覆盖体密度，剂量体积直方图剂量学点的平均偏差在CT值的1%以内，无任何异常值超过2%。2%/2 mm分析的平均伽马通过率高于99%，未观察到低于95%的情况。
本研究展示了在低场混合MR直线加速器腹部区域实施CycleGAN进行sCT生成的过程。结果表明，sCT能够正确分配移动气腔的电子密度，剂量学分析显示了未来腹部仅使用MR放射治疗的应用潜力。
