摘要
医学图像跨模态重建是指基于被试某一种模态图像,预测同一被试的另一种模态图像,以实现更精准的个体化医疗。生成对抗网络(generative adversarial networks,GAN)是医学图像跨模态重建中最常见的深度学习技术,该技术通过从遵循真实数据分布的隐式分布中生成医学图像,进而快速重建出其他模态医学图像数据。随着临床对多模态影像数据需求的剧增,GAN技术在磁共振成像、计算机断层扫描和正电子发射型计算机断层扫描等多种不同的医学图像模态之间的跨模态重建任务中均得到广泛的应用,在脑、心等不同部位实现精准高效的跨模态图像重建。此外,虽然GAN在跨模态重建中取得了一定的成功,但其在稳定性、泛化能力和准确度方面仍需要进一步的改进。
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.
作者
孙杰
金诗晨
石蓉
左传涛
蒋皆恢
SUN Jie;JIN Shichen;SHI Rong;ZUO Chuantao;JIANG Jiehui(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)
出处
《中南大学学报（医学版）》
CAS
CSCD
北大核心
2022年第8期1001-1008,共8页
Journal of Central South University ：Medical Science
基金
国家自然科学基金(82021002,86971641)。
关键词
生成对抗网络
跨模态重建
CT预测
MRI预测
正电子发射型计算机断层扫描预测
generative adversarial networks
cross-modality reconstruction
CT prediction
MRI prediction
positron emission computed tomography prediction
