Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC, USA.
Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.
Comput Biol Med. 2021 Nov;138:104917. doi: 10.1016/j.compbiomed.2021.104917. Epub 2021 Oct 4.
To create synthetic CTs and digital reconstructed radiographs (DRRs) from MR images that allow for fiducial visualization and accurate dose calculation for MR-only radiosurgery.
We developed a machine learning model to create synthetic CTs from pelvic MRs for prostate treatments. This model has been previously proven to generate synthetic CTs with accuracy on par or better than alternate methods, such as atlas-based registration. Our dataset consisted of 11 paired CT and conventional MR (T2) images used for previous CyberKnife (Accuray, Inc) radiotherapy treatments. The MR images were pre-processed to mimic the appearance of fiducial-enhancing images. Two models were trained for each parameter case, using a sub-set of the available image pairs, with the remaining images set aside for testing and validation of the model to identify the optimal patch size and number of image pairs used for training. Four models were then trained using the identified parameters and used to generate synthetic CTs, which in turn were used to generate DRRs at angles 45° and 315°, as would be used for a CyberKnife treatment. The synthetic CTs and DRRs were compared visually and using the mean squared error and peak signal-to-noise ratio against the ground-truth images to evaluate their similarity.
The synthetic CTs, as well as the DRRs generated from them, gave similar visualization of the fiducial markers in the prostate as the true counterparts. There was no significant difference found for the fiducial localization for the CTs and DRRs. Across the 8 DRRs analyzed, the mean MSE between the normalized true and synthetic DRRs was 0.66 ± 0.42% and the mean PSNR for this region was 22.9 ± 3.7 dB. For the full CTs, the mean MAE was 72.9 ± 88.1 HU and the mean PSNR was 31.2 ± 2.2 dB.
Our machine learning-based method provides a proof of concept of a way to generate synthetic CTs and DRRs for accurate dose calculation and fiducial localization for use in radiation treatment of the prostate.
从磁共振图像创建合成 CT 和数字重建射线照片(DRR),以便在仅进行磁共振成像的放射外科手术中进行示踪可视化和准确的剂量计算。
我们开发了一种机器学习模型,可从盆腔磁共振图像中创建前列腺治疗用的合成 CT。该模型已被证明可以生成与替代方法(例如基于图谱的配准)精度相当或更高的合成 CT。我们的数据集由用于以前的 CyberKnife(Accuray,Inc.)放射治疗的 11 对 CT 和常规磁共振(T2)图像组成。预处理 MR 图像以模拟示踪剂增强图像的外观。对于每个参数情况,我们使用可用图像对的子集训练两个模型,将其余图像保留下来用于测试和验证模型,以确定用于训练的最佳补丁大小和图像对数量。然后,使用确定的参数训练了四个模型,并使用这些模型生成合成 CT,然后又使用这些 CT 生成 45°和 315°角度的 DRR,这些角度将用于 CyberKnife 治疗。从视觉上和使用均方误差和峰值信噪比来比较合成 CT 和 DRR,以评估它们的相似性。
合成 CT 以及从中生成的 DRR 与真实 CT 和 DRR 一样,可以很好地显示前列腺中的示踪剂标记。在 CT 和 DRR 中,示踪剂的定位没有发现显著差异。在分析的 8 个 DRR 中,归一化真实和合成 DRR 之间的平均均方误差为 0.66±0.42%,该区域的平均峰值信噪比为 22.9±3.7dB。对于完整的 CT,平均平均绝对误差为 72.9±88.1HU,平均峰值信噪比为 31.2±2.2dB。
我们基于机器学习的方法为生成合成 CT 和 DRR 提供了一种概念验证,可用于准确的剂量计算和前列腺放射治疗中的示踪定位。