MV CBCT-Based Synthetic CT Generation Using a Deep Learning Method for Rectal Cancer Adaptive Radiotherapy.
MV CBCT-Based Synthetic CT Generation Using a Deep Learning Method for Rectal Cancer Adaptive Radiotherapy.
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基于 MV CBCT 的合成 CT 生成，采用深度学习方法进行直肠癌适应性放射治疗
DOI：
10.3389/fonc.2021.655325
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发表时间：
2021
影响因子：
4.7
通讯作者：
Zhang Z
中科院分区：
文献类型：
--
作者：
Zhao J;Chen Z;Wang J;Xia F;Peng J;Hu Y;Hu W;Zhang Z
由于图像质量的限制，代表在线患者解剖结构的在线巨型锥束CT(MV CBCT)不能用于自适应放射治疗(ART)。在这项研究中，我们使用一种深度学习方法-周期一致对抗网络(CycleGAN)来提高直肠癌患者的MV CBCT图像质量和Hounsfield单位(HU)的准确性，以使生成的合成CT(SCT)符合ART要求。40例直肠癌患者接受调强放射治疗(IMRT)。30例患者的CT和MV CBCT图像用于模型训练，其余10例患者的图像用于评估。对生成的SCT的图像质量、自动分割能力和使用自动计划技术的剂量计算能力进行评估。平均绝对误差(MAE)由135.84±41.59HU降至52.99±12.09HU。CT和SCT比较的结构相似性指数(SSIM)为0.81±0.03，比CT和CBCT比较的0.44±0.07有很大提高。股骨头SCT上的自动节段模型表现准确，几乎不需要人工修改。对于CTV和膀胱，虽然需要修改自轮廓，但DSC指数很高，CTV和膀胱的DSC指数分别为0.93和0.94。对于剂量评估，基于SCT的计划与基于CT的计划的剂量偏差比基于CBCT的计划小得多。该方法解决了基于MV CBCT的直肠癌ART实现的关键问题。所生成的SCT基于治疗位置处的实际患者解剖来实现ART。
Due to image quality limitations, online Megavoltage cone beam CT (MV CBCT), which represents real online patient anatomy, cannot be used to perform adaptive radiotherapy (ART). In this study, we used a deep learning method, the cycle-consistent adversarial network (CycleGAN), to improve the MV CBCT image quality and Hounsfield-unit (HU) accuracy for rectal cancer patients to make the generated synthetic CT (sCT) eligible for ART. Forty rectal cancer patients treated with the intensity modulated radiotherapy (IMRT) were involved in this study. The CT and MV CBCT images of 30 patients were used for model training, and the images of the remaining 10 patients were used for evaluation. Image quality, autosegmentation capability and dose calculation capability using the autoplanning technique of the generated sCT were evaluated. The mean absolute error (MAE) was reduced from 135.84 ± 41.59 HU for the CT and CBCT comparison to 52.99 ± 12.09 HU for the CT and sCT comparison. The structural similarity (SSIM) index for the CT and sCT comparison was 0.81 ± 0.03, which is a great improvement over the 0.44 ± 0.07 for the CT and CBCT comparison. The autosegmentation model performance on sCT for femoral heads was accurate and required almost no manual modification. For the CTV and bladder, although modification was needed for autocontouring, the Dice similarity coefficient (DSC) indices were high, at 0.93 and 0.94 for the CTV and bladder, respectively. For dose evaluation, the sCT-based plan has a much smaller dose deviation from the CT-based plan than that of the CBCT-based plan. The proposed method solved a key problem for rectal cancer ART realization based on MV CBCT. The generated sCT enables ART based on the actual patient anatomy at the treatment position.
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影响因子：
3.8
作者：
Liu Y;Lei Y;Wang T;Fu Y;Tang X;Curran WJ;Liu T;Patel P;Yang X
通讯作者：
Yang X
使用锥束计算机断层扫描术中直肠癌术中直肠运动的运动分析。
DOI：
10.1186/s13014-014-0311-6
发表时间：
2015-01-08
期刊：
Radiation oncology (London, England)
影响因子：
--
作者：
Yamashita H;Takenaka R;Sakumi A;Haga A;Otomo K;Nakagawa K
通讯作者：
Nakagawa K
影响因子：
3.8
作者：
Harms J;Lei Y;Wang T;Zhang R;Zhou J;Tang X;Curran WJ;Liu T;Yang X
通讯作者：
Yang X
影响因子：
--
作者：
Zhao J;Hu W;Cai G;Wang J;Xie J;Peng J;Zhang Z
通讯作者：
Zhang Z
影响因子：
3.8
作者：
Klages, Peter;Benslimane, Ilyes;Tyagi, Neelam
通讯作者：
Tyagi, Neelam