Recent developments in magnetic resonance (MR) to synthetic computed tomography (sCT) conversion have shown that treatment planning is possible without an initial planning CT. Promising conversion results have been demonstrated recently using conditional generative adversarial networks (cGANs). However, the performance is generally only tested on images from one MR scanner, which neglects the potential of neural networks to find general high-level abstract features. In this study, we explored the generalizability of the generator models, trained on a single field strength scanner, to data acquired with higher field strengths. T2-weighted 0.35T MRIs and CTs from 51 patients treated for prostate (40) and cervical cancer (11) were included. 25 of them were used to train four different generators (SE-ResNet, DenseNet, U-Net, and Embedded Net). Further, an ensemble model was created from the four network outputs. The models were validated on 16 patients from a 0.35T MR scanner. Further, the trained models were tested on the Gold Atlas dataset, containing T2-weighted MR scans of different field strengths; 1.5T(7) and 3T(12), and 10 patients from the 0.35T scanner. The sCTs were dosimetrically compared using clinical VMAT plans for all test patients. For the same scanner (0.35T), the results from the different models were comparable on the test set, with only minor differences in the mean absolute error (MAE) (35-51HU body). Similar results were obtained for conversions of 3T GE Signa and the 3T GE Discovery images (40-62HU MAE) for three of the models. However, larger differences were observed for the 1.5T images (48-65HU MAE). The overall best model was found to be the ensemble model. All dose differences were below 1%. This study shows that it is possible to generalize models trained on images of one scanner to other scanners and different field strengths. The best metric results were achieved by the combination of all networks.

中文翻译：

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使用不同的生成器体系结构，整体模型和用于MR-sCT转换的不同MR扫描器研究条件GAN性能。

磁共振（MR）到合成计算机断层扫描（sCT）转换的最新进展表明，无需初步计划的CT即可进行治疗计划。最近使用条件生成对抗网络（cGAN）证明了可观的转换结果。但是，通常只在来自一台MR扫描仪的图像上测试性能，而忽略了神经网络发现通用高级抽象特征的潜力。在这项研究中，我们探索了在单个场强扫描仪上训练的生成器模型对具有更高场强的数据的通用性。包括来自51例接受前列腺治疗（40）和宫颈癌（11）的患者的T2加权0.35T MRI和CTs。其中有25个用于训练四个不同的生成器（SE-ResNet，DenseNet，U-Net和Embedded Net）。进一步，从四个网络输出创建了集成模型。通过0.35T MR扫描仪对16例患者进行了模型验证。此外，训练后的模型在Gold Atlas数据集上进行了测试，其中包含不同场强的T2加权MR扫描。1.5T（7）和3T（12），以及10名来自0.35T扫描仪的患者。使用临床VMAT计划对所有受试患者进行sCT剂量学比较。对于同一台扫描仪（0.35T），不同型号的结果在测试集上是可比的，但平均绝对误差（MAE）（35-51HU机身）只有很小的差异。对于三个模型，3T GE Signa和3T GE Discovery图像（40-62HU MAE）的转换获得了相似的结果。但是，对于1.5T图像（48-65HU MAE）观察到较大的差异。发现总体最佳模型是集成模型。所有剂量差异均低于1％。这项研究表明，可以将在一个扫描仪的图像上训练的模型推广到其他扫描仪和不同的场强。通过所有网络的组合可获得最佳的度量结果。