中国临床医学影像杂志2024，Vol.35Issue(1)：56-60,5.DOI:10.12117/jccmi.2024.01.012
基于深度神经网络的高泛化性MR快速成像技术
A fast MR imaging technique with decent generalization performance based on deep neural networks
摘要
Abstract
Objective:To propose a technique for reconstructing undersampled MR images based on deep neural network(DNN)and validate its clinical value.Methods:The main body of the DNN model consisted of two modules:residual convo-lutional network and fidelity network,which could adapt to input images of different sizes and resolutions and effectively learn the noise distribution in the images.A total of 150 volunteers who met the indications for MR scanning were included in this study.K-space full sampling images and accelerated undersampling images were a set of randomly scanned multiple routine sequences of the same subject's head,cervical spine,abdomen,pelvic cavity,and knee joint,totaling 2437 sets of images.Among them,the fully captured images were used as labels without the need for additional annotation.Results:To evaluate the generalization of the DNN-based algorithm,four models were built and trained by changing the input images.The inputs of Model 1 employed all sequences(brain only)other than the current sequence as the output image,while the input of Model 2 was the opposite.The input of Model 3 employed all sequences of various parts(cervical spine,abdomen,pelvic cavity,and knee)other than the current part as the output image,while the inputs of Model 4 were the opposite.The reconstructed re-sults of four models were all very good(SSIM≥0.93,PSNR≥37.22).The average acquisition time was reduced by 16.2%,while the average contrast to noise ratio(CNR)was improved by 8.5%,and the signal to noise ratio(SNR)was improved by more than 7.7%.In addition,the DNN reconstructed images have the same or even higher quality than fully-sampled images.Conclusion:The DNN model can reconstruct high-quality MR images with excellent generalization,which can facilitate fast MR scanning in clinical practice.关键词
神经网/磁共振成像Key words
Nerve Net/Magnetic Resonance Imaging分类
医药卫生引用本文复制引用
余华君,苗帧壮,李瑞阳,陈福军,初占飞,郭红宇,李英飒,李怡,梁晓云..基于深度神经网络的高泛化性MR快速成像技术[J].中国临床医学影像杂志,2024,35(1):56-60,5.基金项目
国家重点研发计划"智能机器人"重点专项(2022YFB4702702). （2022YFB4702702）
