Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network.
Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network.
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DOI：
10.7759/cureus.2548
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发表时间：
2018-04-29
期刊：
影响因子：
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通讯作者：
Nakagawa K
中科院分区：
文献类型：
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作者：
Kida S;Nakamoto T;Nakano M;Nawa K;Haga A;Kotoku J;Yamashita H;Nakagawa K
前言锥束计算机断层扫描(CBCT)在图像引导放射治疗(IGRT)中起着重要的作用，但由于利用散射污染和截断投影重建图像会造成严重的阴影伪影，本文的目的是发展一种用于改善CBCT图像质量的深度卷积神经网络(DCNN)方法。方法选择20例前列腺癌患者的CBCT和计划CT(PCT)图像对。随后，通过图像配准将每个PCT体积预先对准到相应的CBCT体积，从而产生配准的PCT数据(PCTR)。接下来，对一个39层的DCNN模型进行训练，以学习从CBCT到相应的pCTR图像的直接映射。将训练好的模型应用于新的CBCT数据集，得到改进的CBCT(I-CBCT)图像。使用空间非均匀性(SNU)、峰值信噪比(PSNR)和结构相似性指数(SSIM)将I-CBCT图像与pCTR进行比较。结果I-CBCT的图像质量较原CBCT有明显改善，PSNR和SSIM较原CBCT和基于PCT校正的增强CBCT有显著改善。结论建立了一种改善CBCT图像质量的DCNN方法。该方法可直接应用于任何商用CBCT扫描仪采集的CBCT图像。
Introduction Cone beam computed tomography (CBCT) plays an important role in image-guided radiation therapy (IGRT), while having disadvantages of severe shading artifact caused by the reconstruction using scatter contaminated and truncated projections. The purpose of this study is to develop a deep convolutional neural network (DCNN) method for improving CBCT image quality. Methods CBCT and planning computed tomography (pCT) image pairs from 20 prostate cancer patients were selected. Subsequently, each pCT volume was pre-aligned to the corresponding CBCT volume by image registration, thereby leading to registered pCT data (pCTr). Next, a 39-layer DCNN model was trained to learn a direct mapping from the CBCT to the corresponding pCTr images. The trained model was applied to a new CBCT data set to obtain improved CBCT (i-CBCT) images. The resulting i-CBCT images were compared to pCTr using the spatial non-uniformity (SNU), the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). Results The image quality of the i-CBCT has shown a substantial improvement on spatial uniformity compared to that of the original CBCT, and a significant improvement on the PSNR and the SSIM compared to that of the original CBCT and the enhanced CBCT by the existing pCT-based correction method. Conclusion We have developed a DCNN method for improving CBCT image quality. The proposed method may be directly applicable to CBCT images acquired by any commercial CBCT scanner.