基于局部位移校正的磁共振图像相干平均

doi:10.11938/cjmr20162525

波谱学杂志 ›› 2017, Vol. 34 ›› Issue (3): 294-301.doi: 10.11938/cjmr20162525

基于局部位移校正的磁共振图像相干平均

李文静¹, 谢海滨^1,2, 严序³, 周敏雄⁴, 向之明⁵, 杨光^1,2

1. 华东师范大学物理与材料科学学院, 上海市磁共振重点实验室, 上海 200062;
2. 上海卡勒幅磁共振技术有限公司, 上海 201614;
3. 西门子医疗东北亚科研合作部, 上海 201318;
4. 上海健康医学院, 上海 201318;
5. 广州市番禺区中心医院放射科, 广东广州 511400

收稿日期:2016-05-03 修回日期:2017-07-17 出版日期:2017-09-05 发布日期:2017-09-05
通讯作者: 谢海滨,Tel:021-62233873,E-mail:hbxie@phy.ecnu.edu.cn;杨光,Tel:021-62233873,E-mail:gyang@phy.ecnu.edu.cn. E-mail:hbxie@phy.ecnu.edu.cn;gyang@phy.ecnu.edu.cn
基金资助:
国家高技术研究发展计划（"863"计划）资助项目（2014AA123400）.

Magnetic Resonance Image Averaging with Local Offset Correction

LI Wen-jing¹, XIE Hai-bin^1,2, YAN Xu³, ZHOU Min-xiong⁴, XIANG Zhi-ming⁵, YANG Guang^1,2

1. Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, China;
2. Shanghai Colorful Magnetic Resonance Technology Co. Ltd., Shanghai 201614, China;
3. MR Collaboration NE Asia, Siemens Healthcare, Shanghai 201318, China;
4. Shanghai University of Medicine & Health Sciences, Shanghai 201318, China;
5. Department of Radiology, Panyu Center Hospital of Guangzhou, Guangzhou 511400, China

Received:2016-05-03 Revised:2017-07-17 Online:2017-09-05 Published:2017-09-05

摘要/Abstract

摘要： 多次扫描相干平均是提高磁共振图像信噪比的常用方法，但如果在多次扫描过程中病人发生自主或不自主的运动，使得图像中的组织发生位移，简单相干平均图像会导致图像模糊.本文受非局域均值算法的启发，提出了一种基于局部位移校正的相干平均方法.该算法通过比较多次采集的图像中组织结构的局部相似性，找出图像间的局部位移，利用该信息修正位移后进行加权平均，从而达到提高图像信噪比的目的.我们用模型及真实的肝脏弥散数据进行了实验.实验结果表明，对于不同次采样间存在运动的磁共振图像，该算法可有效地提高信噪比并保持结构边缘；其结果优于简单的相干平均，去噪效果也优于经典的非局域均值算法.

关键词: 磁共振成像(MRI), 非局域均值, 图像去噪, 相干平均

Abstract: In magnetic resonance imaging (MRI), data averaging is often used to improve signal-to-noise ratio (SNR) of the images. However, image blurring can be induced by averaging if movements occur during scanning. Inspired by the patch-matching method used in the non-local means algorithm, a new method to find out local offsets of structures in multiple images was proposed by comparing the neighborhood similarities of the image patches. The local offsets could then be corrected before weighted averaging of the images. The performance of the proposed method was verified with both phantom and patient images. The results demonstrated that the proposed algorithm could improve SNR while preserving the image edges and details correctly.

Key words: non-local means, image denoising, averaging, magnetic resonance imaging (MRI)

中图分类号:

O482.53

李文静, 谢海滨, 严序, 周敏雄, 向之明, 杨光. 基于局部位移校正的磁共振图像相干平均[J]. 波谱学杂志, 2017, 34(3): 294-301.

LI Wen-jing, XIE Hai-bin, YAN Xu, ZHOU Min-xiong, XIANG Zhi-ming, YANG Guang. Magnetic Resonance Image Averaging with Local Offset Correction[J]. Chinese Journal of Magnetic Resonance, 2017, 34(3): 294-301.

参考文献

[1] MOHAN J, KRISHNAVENI V, GUO Y. A survey on the magnetic resonance image denoising methods[J]. Biomed Signal Proces, 2014, 9(1):56-69.
[2] JOSHI N, JAIN S. Optimization of nonlocal means filtering technique for denoising magnetic resonance images:A review[C]//Proceedings of fifth international conference on soft computing for problem solving. Singapore:Springer, 2016:1-15.
[3] SAMSONOV A A, JOHNSON C R. Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels[J]. Magn Reson Med, 2004, 52(4):798-806.
[4] LU B B, DENG C, LIU Q, et al. Four order adaptive PDE method for MRI denoising[C]//IEEE ACMT Comput Bi, 2009.
[5] ZHANG F, MA L H. MRI denoising using the anisotropic coupled diffusion equations[C]//20103rd international conference on BMEI. IEEE, 2010, 1:397-401.
[6] MA J, PLONKA G. Combined curvelet shrinkage and nonlinear anisotropic diffusion[J]. IEEE T Image Process, 2007, 16(9):2198-2206.
[7] PARTHIBAN L, SUBRAMANIAN R. Medical image denoising using X-lets[C]//India conference, 2006 Annual IEEE, 2006:1-6.
[8] YANG X, FEI B. A wavelet multiscale denoising algorithm for magnetic resonance (MR) images[J]. Meas Sci Technol, 2011, 22(2):025803.
[9] LUISIER F, BLU T, WOLFE P J. A cure for noisy magnetic resonance images:Chi-square unbiased risk estimation[J]. IEEE T Image Process, 2012, 21(8):3454-3466.
[10] BUADES A, COLL B, MOREL J M. A review of image denoising algorithms, with a new one[J]. Multiscale Model Sim, 2005, 4(2):490-530.
[11] MANJÓN J V, CARBONELL J, LULL J J, et al. MRI denoising using non-local means[J]. Med Image Anal, 2008, 12(4):514-523.
[12] HU J R, PU Y F, WU X, et al. Improved DCT-based nonlocal means filter for MR images denoising[J]. Comput Math Methods Med, 2012:232685.
[13] MANJÓN J V, COUPÉ P, BUADES A, et al. New methods for MRI denoising based on sparseness and self-similarity[J]. Med Image Anal, 2012, 16(1):18-27.
[14] ZHANG X Y, HOU G R, MA J H, et al. Denoising MR images using non-local means filter with combined patch and pixel similarity[J]. PloS one, 2014, 9(6):e100240.
[15] GUO T L, LIU Q G, LUO J H. Filter bank based nonlocal means for denoising magnetic resonance images[J]. Journal of Shanghai Jiaotong University (Science), 2014, 19(1):72-78.
[16] WIEST-DAESSLÉ N, PRIMA S, COUPÉ P, et al. Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI:Applications to DT-MRI[M]. Berlin:Springer Heidelberg, 2008:171-179.
[17] BARNES C, SHECHTMAN E, FINKELSTEIN A, et al. PatchMatch:A randomized correspondence algorithm for structural image editing[J]. ACM Graphic, 2009, 28(3):24.
[18] GIRAUD R, TA V T, PAPADAKIS N, et al. An optimized PatchMatch for multi-scale and multi-feature label fusion[J]. Neuroimage, 2016, 124:770-782.
[19] BARNES C, SHECHTMAN E, GOLDMAN D B, et al. The generalized patchmatch correspondence algorithm[M]. Berlin:Springer Heidelberg, 2010:29-43.
[20] WIEST-DAESSLÉ N, PRIMA S, COUPÉ P, et al. Non-local means variants for denoising of diffusion-weighted and diffusion tensor MRI[M]. Berlin:Springer Heidelberg, 2007:344-351.
[21] BAO L, ROBINI M, LIU W, et al. Structure-adaptive sparse denoising for diffusion-tensor MRI[J]. Med Image Anal, 2013, 17(4):442-457.
[22] MANJÓN J V, COUPÉ P, BUADES A, et al. Non-local MRI upsampling[J]. Med Image Anal, 2010, 14(6):784-792.
[23] http://brainweb.bic.mni.mcgill.ca/brainweb/
[24] BUADES A, COLL B, MOREL J M. A non-local algorithm for image denoising[C]. IEEE T Comput, 2005, 2:60-65.
[25] COUPÉ P, YGER P, PRIMA S, et al. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images[J]. IEEE T Med Imaging, 2008, 27(4):425-441.
[26] AVANAKI A N, DIYANAT A, SODAGARI S. Optimum parameter estimation for non-local means image de-noising using corner information[C]. IEEE T Signal Proce, 2008:861-863.

基于局部位移校正的磁共振图像相干平均

Magnetic Resonance Image Averaging with Local Offset Correction

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	魏国境, 易佩伟, 陶泉, 冯衍秋. CEST成像不同量化方式在急性帕金森氏病小鼠模型研究中的应用比较[J]. 波谱学杂志, 2019, 36(2): 195-207.
[2]	刘颖, 宋明辉, 王坤, 章浩伟. 基于全可编程SoC和LabVIEW的磁共振接收系统设计[J]. 波谱学杂志, 2018, 35(4): 475-485.
[3]	汪红志, 赵地, 杨丽琴, 夏天, 周皛月, 苗志英. 基于AI+MRI的影像诊断的样本增广与批量标注方法[J]. 波谱学杂志, 2018, 35(4): 447-456.
[4]	陈海燕, 赵世龙, 李晓南, 刘国强, 胡丽丽, 刘弢. 低场永磁体磁共振射频场映像[J]. 波谱学杂志, 2018, 35(4): 498-504.
[5]	陶泉, 易佩伟, 魏国境, 冯衍秋. 基于CEST机制的pH成像方法、原理和应用[J]. 波谱学杂志, 2018, 35(4): 505-519.
[6]	翟国强, 张苗, 薄斌仕, 王乙, 范明霞, 李建奇. 基于复值和模值的肝脏磁共振水脂分离组合算法[J]. 波谱学杂志, 2018, 35(4): 417-426.
[7]	黄朝晖, 张志, 陈黎, 陈俊飞, 张震, 陈方, 刘朝阳. MRI梯度预加重模块的分时复用设计[J]. 波谱学杂志, 2018, 35(4): 465-474.
[8]	张波, 谢海滨, 严序, 李文静, 杨光. 旋转不变的非局域均值算法在磁共振图像去噪中的应用[J]. 波谱学杂志, 2018, 35(2): 162-169.
[9]	孙炜航, 孙钰, 汪丰, 王超洪, 杨涛. 变延迟进动定制激发(DANTE)序列在工程中的优化设计[J]. 波谱学杂志, 2018, 35(2): 150-161.
[10]	崔妍, 肖亮. 磁共振成像谱仪B₀信号的高精度发生与测试[J]. 波谱学杂志, 2018, 35(2): 170-177.
[11]	马芸, 郭虹, 王秋实, 张伟国, 吴昊. 基于影像的形态学特征与胶质母细胞瘤特征分子表达的相关性研究[J]. 波谱学杂志, 2018, 35(1): 22-30.
[12]	黄丽洁, 宋阳, 赵献策, 谢海滨, 吴东梅, 杨光. 一种结合并行成像和压缩感知的快速磁共振成像新方法[J]. 波谱学杂志, 2018, 35(1): 31-39.
[13]	罗庆, 张成秀, 李文静, 郑慧, 谢海滨, 杨光. 一种新的磁共振图像处理流水线的设计与实现[J]. 波谱学杂志, 2018, 35(1): 40-51.
[14]	宋瑞, 何砚发, 张波. 一种永磁磁共振中磁体涡流场的测量方法[J]. 波谱学杂志, 2018, 35(1): 52-59.
[15]	李宇宙, 张喆, 陈泉荣, 郭华. 一种以超分辨率理论为基础的磁共振眼球成像方法[J]. 波谱学杂志, 2017, 34(4): 439-452.