基于细节增强的MR序列转换方法

严凡; 邸奕宁; 张建伟; 陈为

doi:10.11959/j.issn.2096-6652.202110

您当前的位置：

首页 >

文章列表页 >

基于细节增强的MR序列转换方法

学术论文 | 更新时间：2024-06-05

- 基于细节增强的MR序列转换方法
- Conversion method of magnetic resonance sequence based on detail enhancement
- 智能科学与技术学报 2021年3卷第1期页码：93-100
- 作者机构：
  
  1. 浙江大学计算机辅助设计与图形学国家重点实验室，浙江杭州 310058
  2. 浙江大学伊利诺伊大学厄巴纳香槟校区联合学院，浙江杭州 314400
- 作者简介：
  
  [ "严凡（1995- ），男，浙江大学计算机辅助设计与图形学国家重点实验室硕士生，主要研究方向为医学影像、深度学习。" ]
  [ "邸奕宁（1998- ），男，浙江大学伊利诺伊大学厄巴纳香槟校区联合学院本科生，主要研究方向为智能交通系统、交通控制与优化、计算机可视分析。" ]
  [ "张建伟（1995- ），男，浙江大学计算机辅助设计与图形学国家重点实验室博士生，主要研究方向为医学影像、深度学习。" ]
  [ "陈为（1976- ），男，博士，浙江大学计算机辅助设计与图形学国家重点实验室教授、博士生导师，中国计算机学会高级会员，主要研究方向为可视化、可视分析、大数据分析、人机混合智能。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202110
  中图分类号： R445.2
- 网络出版日期：2021-03，
  
  纸质出版日期：2021-03-15
- 稿件说明：
移动端阅览
严凡, 邸奕宁, 张建伟, 等. 基于细节增强的MR序列转换方法[J]. 智能科学与技术学报, 2021,3(1):93-100.

Fan YAN, Yining DI, Jianwei ZHANG, et al. Conversion method of magnetic resonance sequence based on detail enhancement[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 93-100.
严凡, 邸奕宁, 张建伟, 等. 基于细节增强的MR序列转换方法[J]. 智能科学与技术学报, 2021,3(1):93-100. DOI： 10.11959/j.issn.2096-6652.202110.

Fan YAN, Yining DI, Jianwei ZHANG, et al. Conversion method of magnetic resonance sequence based on detail enhancement[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 93-100. DOI： 10.11959/j.issn.2096-6652.202110.

摘要

磁共振（MR）是一种被广泛使用的医学成像方式。针对某些序列的 MR 影像不易直接获得、需要由其他序列的MR影像转换的问题，提出一种基于细节增强的MR序列转换方法。该方法基于条件生成对抗网络构建了残差模板合成模块 ResGAN和细节增强合成模块 EnGAN，设计了一种端到端的网络结构。在一组配准过的神经纤维瘤T1序列影像和STIR序列影像上进行了测试，结果表明，相比于现有方法，提出的方法更好地还原了边界细节和信号强度细节。

Abstract

Magnetic resonance (MR) is a widely used medical imaging method.However

MR images of certain modalities are not easy to obtain directly and thus need to be converted from other modality.To solve these problem

a conversion method of MR sequence based on detail enhancement was proposed.The proposed method constructed an end-to-end network with two conditional GAN-based modules: resiguals template generation module (ResGAN) and detail enhancement module (EnGAN).The proposed method are tested on a set of registered neurofibroma T1 and STIR images

which demonstrate that the proposed method can restore boundary details and signal intensity details better than other existing methods.

关键词

Keywords

references

JACK C R , PETERSEN R C , XU Y C , et al . Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment [J ] . Neurology , 1999 , 52 ( 7 ): 1397 - 1397 .

HANRAHAN C J , SHAH L M . MRI of spinal bone marrow:part 2,T1-weighted imaging-based differential diagnosis [J ] . American Journal of Roentgenology , 2011 , 197 ( 6 ): 1309 - 1321 .

EUSTACE S , TELLO R , DECARVALHO V , et al . Whole body turbo STIR MRI in unknown primary tumor detection [J ] . Journal of Magnetic Resonance Imaging , 1998 , 8 ( 3 ): 751 - 753 .

DADAR M , PASCOAL T A , MANITSIRIKUL S , et al . Validation of a regression technique for segmentation of white matter hyperintensities in Alzheimer’s disease [J ] . IEEE Transactions on Medical Imaging , 2017 , 36 ( 8 ): 1758 - 1768 .

PURANIK R , MUTHURANGU V , CELERMAJER D S , et al . Congenital heart disease and multi-modality imaging [J ] . Heart Lung and Circulation , 2010 , 19 ( 3 ): 133 - 144 .

KINGMA D P , WELLING M . Auto-encoding variational bayes [J ] . arXiv preprint , 2013 ,arXiv:1312.6114.

GOODFELLOW I , POUGET-ABADIE J , MIRZA M , et al . Generative adversarial nets [C ] // Proceeding of the Advances in Neural Information Processing Systems .[S.l.:s.n. ] , 2014 : 2672 - 2680 .

MIRZA M , OSINDERO S . Conditional generative adversarial nets [J ] . arXiv preprint , 2014 ,arXiv:1411.1784.

YI X , WALIA E , BABYN P . Generative adversarial network in medical imaging:a review [J ] . Medical Image Analysis , 2019 , 58 : 101552 .

MOUNSAVENG S , VAZQUEZ D , AYED I B , et al . Adversarial learning of general transformations for data augmentation [J ] . arXiv preprint , 2019 ,arXiv:1909.09801.

LEDIG C , THEIS L , HUSZÁR F , et al . Photo-realistic single image super-resolution using a generative adversarial network [C ] // Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2017 : 4681 - 4690 .

BROCK A , DONAHUE J , SIMONYAN K . Large scale GAN training for high fidelity natural image synthesis [J ] . arXiv preprint , 2018 ,arXiv:1809.11096.

LITJENS G , KOOI T , BEJNORDI B E , et al . A survey on deep learning in medical image analysis [J ] . Medical Image Analysis , 2017 , 42 : 60 - 88

NIE D , TRULLO R , LIAN J , et al . Medical image synthesis with context-aware generative adversarial networks [C ] // Proceeding of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention . Cham:Springer , 2017 : 417 - 425 .

CHARTSIAS A , JOYCE T , DHARMAKUMAR R , et al . Adversarial image synthesis for unpaired multi-modal cardiac data [C ] // Proceeding of the 2nd International Workshop on Simulation and Synthesis in Medical Imaging . Cham:Springer , 2017 : 3 - 13 .

HIASA Y , OTAKE Y , TAKAO M , et al . Cross-modality image synthesis from unpaired data using CycleGAN [C ] // Proceeding of the 3rd International Workshop on Simulation and Synthesis in Medical Imaging . Cham:Springer , 2018 : 31 - 41 .

WOLTERINK J M , DINKLA A M , SAVENIJE M H F , et al . Deep MR to CT synthesis using unpaired data [C ] // Proceeding of the 3rd International Workshop on Simulation and Synthesis in Medical Imaging . Cham:Springer , 2017 : 14 - 23 .

WELANDER P , KARLSSON S , EKLUND A . Generative adversarial networks for image-to-image translation on multi-contrast MR images-a comparison of CycleGAN and UNIT [J ] . arXiv preprint , 2018 ,arXiv:1806.07777.

OLUT S , SAHIN Y H , DEMIR U , et al . Generative adversarial training for MRA image synthesis using multi-contrast MRI [C ] // Proceeding of the 1st International Workshop on PRedictive Intelligence in Medicine . Cham:Springer , 2018 : 147 - 154 .

HAN C , HAYASHI H , RUNDO L , et al . GAN-based synthetic brain MR image generation [C ] // Proceeding of the 2018 IEEE 15th International Symposium on Biomedical Imaging . Piscataway:IEEE Press , 2018 : 734 - 738 .

DAR S U H , YURT M , KARACAN L , et al . Image synthesis in multi-contrast MRI with conditional generative adversarial networks [J ] . IEEE Transactions on Medical Imaging , 2019 , 38 ( 10 ): 2375 - 2388 .

YU B T , WANG L P , SHI L , et al . Ea-GANs:edge-aware generative adversarial networks for cross-modality MR image synthesis [J ] . IEEE Transactions on Medical Imaging , 2019 , 38 ( 7 ): 1750 - 1762 .

RONNEBERGER O , FISCHER P , BROX T . U-Net:Convolutional networks for biomedical image segmentation [C ] // Proceeding of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention . Cham:Springer , 2015 : 234 - 241 .

ULYANOV D , VEDALDI A , LEMPITSKY V . Instance normalization:The missing ingredient for fast stylization [J ] . arXiv preprint , 2016 ,arXiv:1607.08022.

MAAS A L , HANNUN A Y , NG A Y . Rectifier nonlinearities improve neural network acoustic models [C ] // Proceeding of the 30th International Conference on Machine Learning .[S.l.:s.n. ] , 2013 .

WANG Z , BOVIK A C , SHEIKH H R , et al . Image quality assessment:from error visibility to structural similarity [J ] . IEEE Transactions on Image Processing , 2004 , 13 ( 4 ): 600 - 612 .

浏览量

844

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于半监督学习的人脸识别反欺骗方法研究

基于半监督生成对抗网络的三维重建云工作室