Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444.
PET Center, Huashan Hospital Affiliated to Fudan University, Shanghai 200040, China. zuochuantao@ fudan.edu.cn.
Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2022 Aug 28;47(8):1001-1008. doi: 10.11817/j.issn.1672-7347.2022.220189.
Cross-modality reconstruction of medical images refers to predicting the image from one modality to another so as to achieve more accurate personalized medicine. Generative adversarial networks is the most commonly used deep learning technique in cross-modality reconstruction. It can generate realistic images by learning features from implicit distributions that follow the distributions of real data and then reconstruct the image of another modality rapidly. With the sharp increase in clinical demand for multi-modality medical image, this technology has been widely used in the task of cross modal reconstruction between different medical image modalities, such as magnetic resonance imaging, computed tomography and positron emission computed tomography. It can achieve accurate and efficient cross-modality image reconstruction in different parts of the body, such as the brain, heart, etc. In addition, although GAN has achieved some success in cross-modality reconstruction, its stability, generalization ability, and accuracy still need further research and improvement.
医学图像跨模态重建是指从一种模态预测另一种模态的图像，从而实现更准确的个性化医疗。生成对抗网络是跨模态重建中最常用的深度学习技术。它可以通过从隐含分布中学习特征来生成逼真的图像，这些隐含分布遵循真实数据的分布，然后快速重建另一种模态的图像。随着临床对多模态医学图像需求的急剧增加，这项技术已广泛应用于不同医学图像模态之间的跨模态重建任务，如磁共振成像、计算机断层扫描和正电子发射断层扫描。它可以在身体的不同部位（如大脑、心脏等）实现精确高效的跨模态图像重建。此外，尽管 GAN 在跨模态重建中取得了一些成功，但它的稳定性、泛化能力和准确性仍需要进一步的研究和改进。
