Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas, USA.
Med Phys. 2023 Jul;50(7):4399-4414. doi: 10.1002/mp.16246. Epub 2023 Feb 4.
MR scans used in radiotherapy can be partially truncated due to the limited field of view (FOV), affecting dose calculation accuracy in MR-based radiation treatment planning.
We proposed a novel Compensation-cycleGAN (Comp-cycleGAN) by modifying the cycle-consistent generative adversarial network (cycleGAN), to simultaneously create synthetic CT (sCT) images and compensate the missing anatomy from the truncated MR images.
Computed tomography (CT) and T1 MR images with complete anatomy of 79 head-and-neck patients were used for this study. The original MR images were manually cropped 10-25 mm off at the posterior head to simulate clinically truncated MR images. Fifteen patients were randomly chosen for testing and the rest of the patients were used for model training and validation. Both the truncated and original MR images were used in the Comp-cycleGAN training stage, which enables the model to compensate for the missing anatomy by learning the relationship between the truncation and known structures. After the model was trained, sCT images with complete anatomy can be generated by feeding only the truncated MR images into the model. In addition, the external body contours acquired from the CT images with full anatomy could be an optional input for the proposed method to leverage the additional information of the actual body shape for each test patient. The mean absolute error (MAE) of Hounsfield units (HU), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between sCT and real CT images to quantify the overall sCT performance. To further evaluate the shape accuracy, we generated the external body contours for sCT and original MR images with full anatomy. The Dice similarity coefficient (DSC) and mean surface distance (MSD) were calculated between the body contours of sCT and original MR images for the truncation region to assess the anatomy compensation accuracy.
The average MAE, PSNR, and SSIM calculated over test patients were 93.1 HU/91.3 HU, 26.5 dB/27.4 dB, and 0.94/0.94 for the proposed Comp-cycleGAN models trained without/with body-contour information, respectively. These results were comparable with those obtained from the cycleGAN model which is trained and tested on full-anatomy MR images, indicating the high quality of the sCT generated from truncated MR images by the proposed method. Within the truncated region, the mean DSC and MSD were 0.85/0.89 and 1.3/0.7 mm for the proposed Comp-cycleGAN models trained without/with body contour information, demonstrating good performance in compensating the truncated anatomy.
We developed a novel Comp-cycleGAN model that can effectively create sCT with complete anatomy compensation from truncated MR images, which could potentially benefit the MRI-based treatment planning.
由于视野（FOV）有限，放射治疗中使用的磁共振（MR）扫描可能会部分截断，从而影响基于 MR 的放射治疗计划中的剂量计算准确性。
我们通过修改循环一致生成对抗网络（cycleGAN），提出了一种新的补偿循环 Gan（Comp-cycleGAN），以同时创建合成 CT（sCT）图像并补偿截断 MR 图像中丢失的解剖结构。
本研究使用了 79 例头颈部患者的完整解剖结构的 CT 和 T1 MR 图像。原始的 MR 图像通过手动在后头部裁剪 10-25mm 来模拟临床截断的 MR 图像。随机选择 15 例患者进行测试，其余患者用于模型训练和验证。在 Comp-cycleGAN 训练阶段同时使用截断和原始 MR 图像，使模型能够通过学习截断与已知结构之间的关系来补偿缺失的解剖结构。训练完成后，仅将截断的 MR 图像输入模型，即可生成具有完整解剖结构的 sCT 图像。此外，从具有完整解剖结构的 CT 图像获得的外部身体轮廓也可以作为建议方法的可选输入，以利用每个测试患者的实际身体形状的附加信息。通过计算 sCT 和真实 CT 图像之间的平均绝对误差（MAE）、峰值信噪比（PSNR）和结构相似性指数（SSIM），定量评估 sCT 的整体性能。为了进一步评估形状精度，我们为具有完整解剖结构的 sCT 和原始 MR 图像生成了外部身体轮廓。计算 sCT 和原始 MR 图像的截断区域的体轮廓之间的 Dice 相似系数（DSC）和平均表面距离（MSD），以评估解剖结构补偿的准确性。
在测试患者中计算的平均 MAE、PSNR 和 SSIM 分别为 93.1HU/91.3HU、26.5dB/27.4dB 和 0.94/0.94，用于训练无/有身体轮廓信息的建议 Comp-cycleGAN 模型。这些结果与在全解剖 MR 图像上进行训练和测试的 cycleGAN 模型获得的结果相当，表明该方法从截断的 MR 图像中生成的 sCT 质量很高。在截断区域内，用于训练无/有身体轮廓信息的建议 Comp-cycleGAN 模型的平均 DSC 和 MSD 分别为 0.85/0.89 和 1.3/0.7mm，表明在补偿截断解剖结构方面具有良好的性能。
我们开发了一种新的 Comp-cycleGAN 模型，可以有效地从截断的 MR 图像中创建具有完整解剖结构补偿的 sCT，这可能有助于基于 MRI 的治疗计划。
