Purpose

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast, which makes it useful for delineating tumor and normal structures in radiation therapy planning, but MRI cannot readily provide electron density for dose calculation. Computed tomography (CT) is used but introduces registration uncertainty between MRI and CT. Previous studies have shown that synthetic CTs (sCTs) can be generated directly from MRI images with deep learning methods. However, mainly high-field MRI images have been validated. This study tested whether acceptable sCTs for MR-only radiation therapy planning can be synthesized using an integrated MR-guided linear accelerator at 0.35T, using MRI images and treatment plans in the liver region.

Methods and Materials

Two models were investigated in this study: a convolutional neural network (Unet) with conventional mean square error (MSE) loss and a Unet using a secondary convolutional neural network for perceptual loss. A total of 37 cases were used in this study with 10-fold cross validation, and 37 treatment plans were generated and evaluated for target coverage and dose to organs at risk (OARs) in the MSE loss model, perceptual loss model, and original CT.

Results

The sCTs predicted by the perceptual loss model had improved subjective visual quality compared with those predicted by the MSE loss model, but both were similar in mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The MAE, PSNR, and NCC for the perceptual loss model were 35.64, 24.11, and 0.9539, respectively, and those for the MSE loss model were 35.67, 24.36, and 0.9566, respectively. No significant differences in target coverage and dose to OARs were found between the sCT predicted by the perceptual loss model or by the MSE model and the original CT image.

Conclusions

This study indicated that a Unet with both MSE loss and perceptual loss models can be used for generating sCT images from a 0.35T integrated MR linear accelerator.

中文翻译：

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从 0.35T 磁共振图像生成合成计算机断层扫描，用于使用感知损失模型进行仅磁共振放射治疗计划

目的

磁共振成像 (MRI) 提供出色的软组织对比，这使其可用于在放射治疗计划中描绘肿瘤和正常结构，但 MRI 无法轻易提供用于剂量计算的电子密度。使用计算机断层扫描 (CT)，但会在 MRI 和 CT 之间引入配准不确定性。先前的研究表明，可以使用深度学习方法直接从 MRI 图像生成合成 CT (sCT)。然而，主要是高场 MRI 图像得到了验证。本研究使用肝脏区域的 MRI 图像和治疗计划，测试是否可以使用 0.35T 的集成 MR 引导线性加速器合成可接受的仅 MR 放射治疗计划的 sCT。

方法和材料

本研究调查了两个模型：具有常规均方误差 (MSE) 损失的卷积神经网络 (Unet) 和使用二次卷积神经网络进行感知损失的 Unet。本研究共使用 37 个病例进行 10 折交叉验证，生成 37 个治疗方案，并在 MSE 损失模型、知觉损失模型和原始 CT 中评估目标覆盖率和风险器官 (OARs) 剂量.

结果

与 MSE 损失模型预测的相比，感知损失模型预测的 sCT 具有改善的主观视觉质量，但两者在平均绝对误差 (MAE)、峰值信噪比 (PSNR) 和归一化交叉方面相似-相关性（NCC）。感知损失模型的MAE、PSNR和NCC分别为35.64、24.11和0.9539，MSE损失模型分别为35.67、24.36和0.9566。在由感知损失模型或 MSE 模型预测的 sCT 与原始 CT 图像之间，未发现目标覆盖率和 OAR 剂量存在显着差异。

结论

这项研究表明，具有 MSE 损失和感知损失模型的 Unet 可用于从 0.35T 集成 MR 直线加速器生成 sCT 图像。