| 陈杰,王克强,简建波,王鹏,吴君,张文学.投影域与图像域联合学习重建网络用于重建有限角度胸部CT图像[J].中国医学影像技术,2024,40(9):1410~1416 |
| 投影域与图像域联合学习重建网络用于重建有限角度胸部CT图像 |
| Projection domain and image domain joint learning reconstruction network for reconstructing chest limited angle CT images |
| 投稿时间：2024-02-27 修订日期：2024-05-23 |
| DOI：10.13929/j.issn.1003-3289.2024.09.028 |
| 中文关键词: 胸部 深度学习 体层摄影术,X线计算机 |
| 英文关键词:thorax deep learning tomography, X-ray computed |
| 基金项目: |
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| 摘要点击次数: 506 |
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| 中文摘要: |
| 目的 观察双域(投影域与图像域)联合学习重建网络(DDRNet)用于重建胸部有限角度CT图像的价值。方法 回顾性收集65例胸部肿瘤患者共4 300幅胸部增强CT图像,以DDRNet进行重建,并将三维与二维投影信息融合;评估重建效果,并与单域重建,滤波反投影法(FBP)、基于残差的编码/解码网络(RED-CNN)、Resnet+反卷积网络(RDN)及生成对抗网络(GAN)重建结果进行比较。结果 DDRNet重建图像峰值信噪比(PSNR)于迭代约60轮后,而投影域和图像域学习网络则于迭代约90轮和80轮后趋于稳定。训练稳定后,相比投影域学习网络,DDRNet和图像域学习网络输出结果波动更小;训练200轮后,DDRNet重建图像PSNR显著高于投影域和图像域学习网络。DDRNet重建图像质量明显优于FBP、RED-CNN、RDN及GAN。结论 DDRNet可有效重建高质量胸部有限角度CT图像。 |
| 英文摘要: |
| Objective To observe the value of dual domain (projection domain and image domain) joint learning reconstruction network (DDRNet) for reconstructing chest limited angle CT images. Methods Totally 4 300 chest enhanced CT images of 65 patients with chest tumors were retrospectively enrolled and reconstructed with DDRNet, and 3D and 2D projection information fusion were performed. The reconstruction effect of DDRNet was evaluated and compared with that of single domain reconstruction and filtered back projection (FBP), residual encoder-decoder convolutional neural network (RED-CNN), Resnet and deconvolution network (RDN), as well as of generative adversarial network (GAN). Results The peak signal to noise ratio (PSNR) of DDRNet reconstructed images tended to stabilize after approximately 60 iterations, while the projection domain and image domain learning networks tended to stabilize after approximately 90 and 80 iterations. After stable training, compared to the projection domain learning network, the fluctuation of output results of DDRNet and image domain learning networks were less. After 200 rounds of training, PSNR of DDRNet reconstructed images was significantly higher than that of projection domain and image domain learning networks. The quality of DDRNet reconstructed image was significantly better than that of FBP, RED-CNN, RDN and GAN. Conclusion DDRNet could be used to effectively reconstruct high-quality chest limited angle CT images. |
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