Abstract

Info

Author(s):: DONG Guoya1; 2; SONG Liming1; 2; 3; LI Yafen3; LI Wen3; XIE Yaoqin3; 1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300132, China 2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300132, China 3. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 440305, China

Keywords:: Keywords: deep learning computed tomography magnetic resonance imaging U-Net convolutional neural network cross-modality image synthesis synthetic magnetic resonance imaging

Abstract:: Abstract: The purpose of this research is to synthesize the corresponding magnetic resonance imaging (MRI) based on brain computed tomography (CT) images by deep learning. The tomographic images of CT and MRI obtained by brain CT and MRI scanning are rigidly registered in 28 patients, and the images of 20 patients are randomly input into U-Net convolutional neural network for training. The CT images of 8 patients who do not participate in the training are predicted by the trained network, thereby obtaining the synthetic MRI. The results reveal that through the quantitative analysis on synthetic MRI, the U-Net network constructed based on L2 loss function has a good performance in synthesizing MRI, with a mean absolute error of 47.81 and an average structural similarity index of 0.91. This study shows that deep learning method can be used to obtain synthetic MRI by converting CT images, thus achieving the purpose of expanding MRI medical database. With the improvement of the accuracy of image synthesis, it can be used in diagnosis and other clinical applications.