基于深度学习的磁共振液体衰减反转恢复序列图像合成方法  

Deep learning method for magnetic resonance imaging fluid-attenuated inversion recovery image synthesis

作　　者：周家柠 郭红宇[1,2] 陈红 ZHOU Jianing;GUO Hongyu;CHEN Hong(School of Electrical Engineering,Shenyang University of Technology,Shenyang l10870,P.R.China;Neusoft Medical System Co.Ltd,Shenyang 110167,P.R.China)

机构地区：[1]沈阳工业大学电气工程学院,沈阳110870 [2]东软医疗系统股份有限公司,沈阳110167

出　　处：《生物医学工程学杂志》2023年第5期903-911,共9页Journal of Biomedical Engineering

基　　金：国家重点研发计划项目(2022YFB4702700);国家自然科学基金项目(61771323);辽宁省教育厅科学研究经费项目(LJKZ0132)。

摘　　要：磁共振能够获得不同对比度的多模态图像,为临床诊断提供了丰富的信息。但是常常由于患者难以配合或扫描条件限制造成某些对比度图像没有被扫描或者获得的图像质量不能达到诊断要求。图像合成技术是弥补这种图像缺失的一种方法。近年来,深度学习在磁共振图像合成领域得到了广泛应用。本文提出了一种基于多模态融合的合成网络,首先利用特征编码器将多个单模态图像分别进行特征编码后,再通过特征融合模块将不同模态图像特征进行融合,最终生成目标模态图像。通过引入基于图像域和K空间域的动态加权组合损失函数,改进了网络中目标图像与预测图像的相似性度量方法。经实验验证并定量比较,本文提出的多模态融合深度学习网络可以有效合成高质量的磁共振液体衰减反转恢复(FLAIR)序列图像。综上,本文提出的方法可以减少患者的磁共振扫描时间,以及解决FLAIR图像缺失或图像质量难以满足诊断要求的临床问题。Magnetic resonance imaging(MRI)can obtain multi-modal images with different contrast,which provides rich information for clinical diagnosis.However,some contrast images are not scanned or the quality of the acquired images cannot meet the diagnostic requirements due to the difficulty of patient's cooperation or the limitation of scanning conditions.Image synthesis techniques have become a method to compensate for such image deficiencies.In recent years,deep learning has been widely used in the field of MRI synthesis.In this paper,a synthesis network based on multi-modal fusion is proposed,which firstly uses a feature encoder to encode the features of multiple unimodal images separately,and then fuses the features of different modal images through a feature fusion module,and finally generates the target modal image.The similarity measure between the target image and the predicted image in the network is improved by introducing a dynamic weighted combined loss function based on the spatial domain and K-space domain.After experimental validation and quantitative comparison,the multi-modal fusion deep learning network proposed in this paper can effectively synthesize high-quality MRI fluid-attenuated inversion recovery(FLAIR)images.In summary,the method proposed in this paper can reduce MRI scanning time of the patient,as well as solve the clinical problem of missing FLAIR images or image quality that is difficult to meet diagnostic requirements.

关 键 词：磁共振成像 深度学习 多模态 特征融合 

分 类 号：R445.2[医药卫生—影像医学与核医学]