MRI Generated From CT for Acute Ischemic Stroke Combining Radiomics and Generative Adversarial Networks

人工智能 特征(语言学) 计算机科学 串联(数学) 磁共振成像 模式识别(心理学) 光学(聚焦) 放射科 相似性(几何) 医学 病变 图像(数学) 数学 病理 哲学 物理 光学 组合数学 语言学
作者
Eryan Feng,Pinle Qin,Rui Chai,Jianchao Zeng,Qi Wang,Yanfeng Meng,Peng Wang
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:26 (12): 6047-6057 被引量:22
标识
DOI:10.1109/jbhi.2022.3205961
摘要

Compared to computed tomography (CT), magnetic resonance imaging (MRI) is more sensitive to acute ischemic stroke lesion. However, MRI is time-consuming, expensive, and susceptible to interference from metal implants. Generating MRI images from CT images can address the limitations of MRI. The key problem in the process is obtaining lesion information from CT. In this study, we propose a cross-modal image generation algorithm from CT to MRI for acute ischemic stroke by combining radiomics with generative adversarial networks. First, the lesion candidate region was obtained using radiomics, the radiomic features of the region were extracted, and the feature with the largest information gain was selected and visualized as a feature map. Then, the concatenation of the extracted feature map and the CT image was input in the generator. We added a residual module after the downsampling of the generator, following the general shape of U-Net, which can deepen the network without causing degradation problems. In addition, we introduced the lesion feature similarity loss function to focus the model on the similarity of the lesion. Through the subjective judgment of two experienced radiologists and using evaluation metrics, the results showed that the generated MRI images were very similar to the real MRI images. Moreover, the locations of the lesions were correct, and the shapes of lesions were similar to those of the real lesions, which can help doctors with timely diagnosis and treatment.
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