Synthetic Computed Tomography Generation from 0.35T Magnetic Resonance Images for Magnetic Resonance-Only Radiation Therapy Planning Using Perceptual Loss Models.
Synthetic Computed Tomography Generation from 0.35T Magnetic Resonance Images for Magnetic Resonance-Only Radiation Therapy Planning Using Perceptual Loss Models.
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DOI：
10.1016/j.prro.2021.08.007
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发表时间：
2022-01
影响因子：
3.3
通讯作者：
McMillan AB
中科院分区：
文献类型：
--
作者：
Li X;Yadav P;McMillan AB
磁共振成像（MRI）提供了良好的软组织对比度，这使得它在放射治疗计划中用于描绘肿瘤和正常结构，但MRI不能轻易提供用于剂量计算的电子密度。使用CT，但在MRI和CT之间引入配准不确定性。以前的研究已经证明了使用深度学习方法直接从MRI图像生成的合成CT（sCT）。然而，主要是高场MRI图像已得到验证。本研究旨在测试是否可以使用0.35 T的集成MR-Linac，使用肝脏区域的MRI图像和治疗计划合成仅MR放射治疗计划的可接受sCT。在这项研究中，两个模型进行了研究，一个Unet与传统的均方误差（MSE）的损失和一个Unet利用二次VGG 16网络的感知损失。本研究使用了37例病例，并进行了10倍交叉验证。生成了37个治疗计划，并在MSE损失模型、感知损失模型和原始CT上评价了目标覆盖率和危及器官（OAR）剂量。与MSE损失模型相比，感知损失模型预测的sCT改善了主观视觉质量，但两者在MAE，PSNR或NCC方面相似。感知损失模型的MAE、PSNR和NCC分别为35.64、24.11和0.9539，而MSE损失模型的MAE、PSNR和NCC分别为35.67、24.36和0.9566。感知损失模型或MSE模型预测的sCT与原始CT在靶区覆盖率和OAR剂量方面无显著差异。该研究表明，具有MSE损失和感知损失模型的Unet可用于从0.35T集成MR-Linac生成sCT图像。
Magnetic resonance imaging (MRI) provides excellent soft tissue contrast which makes it useful for delineating tumor and normal structures in radiotherapy planning, but MRI cannot readily provide electron density for dose calculation. CT is used but introduces registration uncertainty between MRI and CT. Previous studies have demonstrated synthetic CTs (sCTs) generated directly from MRI images with deep learning methods. However, mainly high-field MRI images have been validated. This study is to test whether acceptable sCTs for MR-only radiation therapy planning can be synthesized using an integrated MR-Linac at 0.35T, using MRI images and treatment plans in the liver region. Two models were investigated in this study, a Unet with conventional mean square error (MSE) loss and a Unet utilizing a secondary VGG16 network for perceptual loss. 37 cases were utilized in this study with ten-fold cross validation. 37 treatment plans were generated and evaluated for target coverage and dose to organs at risk (OARs) on the MSE loss model, perceptual loss model, and original CT. The sCTs predicted by the perceptual loss model had improved subjective visual quality compared to the MSE loss model, but both were similar in MAE, PSNR, or NCC. The MAE, PSNR, and NCC for perceptual loss model were 35.64, 24.11, and 0.9539 while those for MSE loss model were 35.67, 24.36, and 0.9566. No significant differences in target coverage and dose to OARs were found between the sCT predicted by perceptual loss model or by MSE model and the original CT. This study indicates that a Unet with both MSE loss and perceptual loss models can be used for generating sCT images from a 0.35T integrated MR-Linac.
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DOI：
10.1016/j.ejmp.2017.02.017
发表时间：
2017-03
期刊：
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
影响因子：
--
作者：
Guerreiro F;Burgos N;Dunlop A;Wong K;Petkar I;Nutting C;Harrington K;Bhide S;Newbold K;Dearnaley D;deSouza NM;Morgan VA;McClelland J;Nill S;Cardoso MJ;Ourselin S;Oelfke U;Knopf AC
通讯作者：
Knopf AC
影响因子：
5.7
作者：
Cusumano, Davide;Lenkowicz, Jacopo;Valentini, Vincenzo
通讯作者：
Valentini, Vincenzo
影响因子：
4.7
作者：
Wang, Yuenan;Liu, Chenbin;Deng, Weiwei
通讯作者：
Deng, Weiwei
影响因子：
3.5
作者：
Liu Y;Lei Y;Wang Y;Shafai-Erfani G;Wang T;Tian S;Patel P;Jani AB;McDonald M;Curran WJ;Liu T;Zhou J;Yang X
通讯作者：
Yang X
使用生成对抗网络进行基于多序列 MR 图像的合成 CT 生成，仅用于头颈 MRI 放射治疗
影响因子：
3.8
作者：
Qi, Mengke;Li, Yongbao;Song, Ting
通讯作者：
Song, Ting