Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography.
Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography.
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DOI：
10.1002/mp.13656
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发表时间：
2019-09
期刊：
影响因子：
3.8
通讯作者：
Yang X
中科院分区：
文献类型：
--
作者：
Harms J;Lei Y;Wang T;Zhang R;Zhou J;Tang X;Curran WJ;Liu T;Yang X
锥形束计算机断层扫描（CBCT）的结合允许增强的图像引导放射治疗。虽然CBCT允许每日3D成像，但图像受到严重伪影的影响，限制了CBCT的临床潜力。在这项工作中，提出了一种基于深度学习的方法来生成高质量的校正CBCT（CCBCT）图像。所提出的方法将残差块概念集成到循环一致对抗网络（cycle-GAN）框架（称为res-cycle GAN）中，以学习CBCT图像和配对计划CT图像之间的映射。与GAN相比，循环GAN包括从CBCT到CT图像的逆变换，其通过强制计算CCBCT和合成CBCT来约束模型。在生成器中使用具有残差块的全卷积神经网络来实现端到端CBCT到CT变换。所提出的算法进行了评估，使用24组患者的数据在大脑和20组患者的数据在骨盆。采用平均绝对误差（MAE）、峰值信噪比（PSNR）、归一化互相关（NCC）和空间非均匀性（SNU）指标对算法的校正精度进行了量化。所提出的方法相比，传统的散射校正和另一种基于机器学习的CBCT校正方法。总体而言，对于所提出的方法，大脑中的MAE、PSNR、NCC和SNU分别为13.0 HU、37.5 dB、0.99和0.05，骨盆中的MAE、PSNR、NCC和SNU分别为16.1 HU、30.7 dB、0.98和0.09，大脑中的改善分别为45%、16%、1%和93%，骨盆中的改善分别为71%、38%、2%和65%。在CBCT图像上。与散射校正方法相比，所提出的方法显示出上级图像质量，降低了噪声和伪影的严重程度。所提出的方法产生的图像比基于机器学习的方法具有更少的噪声和伪影。作者开发了一种新的基于深度学习的方法来生成高质量的校正CBCT图像。所提出的方法提高了机载CBCT图像质量，使其与计划CT的图像质量相当。随着进一步的评估和临床实施，这种方法可能导致定量自适应放射治疗。
The incorporation of cone-beam computed tomography (CBCT) has allowed for enhanced image-guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning-based method for generating high quality corrected CBCT (CCBCT) images is proposed. The proposed method integrates a residual block concept into a cycle-consistent adversarial network (cycle-GAN) framework, called res-cycle GAN, to learn a mapping between CBCT images and paired planning CT images. Compared with a GAN, a cycle-GAN includes an inverse transformation from CBCT to CT images, which constrains the model by forcing calculation of both a CCBCT and a synthetic CBCT. A fully convolution neural network with residual blocks is used in the generator to enable end-to-end CBCT-to-CT transformations. The proposed algorithm was evaluated using 24 sets of patient data in the brain and 20 sets of patient data in the pelvis. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were used to quantify the correction accuracy of the proposed algorithm. The proposed method is compared to both a conventional scatter correction and another machine learning-based CBCT correction method. Overall, the MAE, PSNR, NCC, and SNU were 13.0 HU, 37.5 dB, 0.99, and 0.05 in the brain, 16.1 HU, 30.7 dB, 0.98, and 0.09 in the pelvis for the proposed method, improvements of 45%, 16%, 1%, and 93% in the brain, and 71%, 38%, 2%, and 65% in the pelvis, over the CBCT image. The proposed method showed superior image quality as compared to the scatter correction method, reducing noise and artifact severity. The proposed method produced images with less noise and artifacts than the comparison machine learning-based method. The authors have developed a novel deep learning-based method to generate high-quality corrected CBCT images. The proposed method increases onboard CBCT image quality, making it comparable to that of the planning CT. With further evaluation and clinical implementation, this method could lead to quantitative adaptive radiation therapy.
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影响因子：
10.6
作者：
Huynh T;Gao Y;Kang J;Wang L;Zhang P;Lian J;Shen D;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
基于深度学习技术预测三维剂量分布的自动治疗计划
影响因子：
3.8
作者：
Fan, Jiawei;Wang, Jiazhou;Hu, Weigang
通讯作者：
Hu, Weigang
影响因子：
3.8
作者：
Shi, Linxi;Tsui, Tiffany;Zhu, Lei
通讯作者：
Zhu, Lei
DOI：
10.7759/cureus.2548
发表时间：
2018-04-29
期刊：
Cureus
影响因子：
--
作者：
Kida S;Nakamoto T;Nakano M;Nawa K;Haga A;Kotoku J;Yamashita H;Nakagawa K
通讯作者：
Nakagawa K
影响因子：
3.5
作者：
Valdes, Gilmer;Solberg, Timothy D.;Simone, Charles B., II
通讯作者：
Simone, Charles B., II