| 王江涛,吴新红,闫冰,朱磊,杨益东.一种基于互信息对比学习由磁共振成像生成头部伪CT图像的方法[J].中华放射医学与防护杂志,2022,42(2):95-102 |
| 一种基于互信息对比学习由磁共振成像生成头部伪CT图像的方法 |
| Mutual information-based contrastive learning for the generation of pseudo-CT images of the head from magnetic resonance imaging |
| 投稿时间：2021-08-10 |
| DOI：10.3760/cma.j.cn112271-20210810-00318 |
| 中文关键词: 磁共振成像 对比学习 伪CT |
| 英文关键词:Magnetic resonance imaging Contrastive learning Pseudo-CT |
| 基金项目:中央高校基本科研业务费专项资金（WK2030000037、WK2030040089）；安徽省科技重大专项（BJ2030480006）；国家自然科学基金（81671681）；科技部重点研发计划项目（2016YFC0101400） |
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| 中文摘要: |
| 目的 比较不同神经网络由磁共振成像（MRI）图像生成伪CT图像的本领，探讨伪CT用于临床放疗计划的可行性。方法 选取29例同时具有计划CT和诊断MRI的脑癌患者，23例用于训练，6例用于测试。分别采用循环生成对抗网络（cycleGAN）、对比学习非配对图像转换网络（CUT）以及本研究提出的改进网络denseCUT由MRI生成伪CT，并将伪CT导入治疗计划系统中验证其用于放疗计划的可行性。结果 CycleGAN、CUT和denseCUT生成的伪CT与真实CT之间的平均绝对误差分别为（72.0±6.9）、（72.5±8.0）和（64.6±7.3） HU，结构相似性分别为0.91±0.01、0.91±0.01和0.93±0.01，峰值信噪比分别为（28.5±0.7）、（28.5±0.7）和（29.5±0.7） dB，放疗计划剂量计算γ通过率（2%/2 mm）分别为98.05%、97.92%、98.31%。结论 denseCUT能更准确地生成伪CT，伪CT能满足调强放疗计划剂量计算的需求。 |
| 英文摘要: |
| Objective To compare the abilities of different neural networks to generate pseudo-computed tomography (CT) images from magnetic resonance imaging (MRI) images and to explore the feasibility of pseudo-CT for clinical radiotherapy planning. Methods A total of 29 brain cancer patients with planning CT and diagnostic MRI were selected. 23 of these patients were used for training neural networks and 6 for testing pseudo-CT images. Cycle-consistent generative adversarial network (cycleGAN), contrastive learning for unpaired image-to-image translation (CUT), and improved network denseCUT proposed in this study were applied to generate pseudo-CT images from MRI images. The pseudo-CT images were imported into a clinical treatment planning system to verify the feasibility of applying this method to radiotherapy planning. Results The comparison between the generated pseudo-CT images and real CT images showed that the mean absolute errors were (72.0±6.9),(72.5±8.0), and (64.6±7.3) HU for the cycleGAN, CUT, and denseCUT, respectively. Meanwhile, the structure similarity indices were 0.91±0.01, 0.91±0.01, and 0.93±0.01, respectively. The peak signal-to-noise ratios were (28.5±0.7), (28.5±0.7), and (29.5±0.7) dB, respectively. The 2%/2 mm γ passing rates were 98.05%, 97.92%, and 98.31% for the cycleGAN, CUT, and denseCUT, respectively. Conclusions DenseCUT can generate more accurate pseudo-CT images and the pseudo-CT can meet the demand for the dose calculation of IMRT plan. |
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