基于群稀疏理论的乳腺动态对比度增强核磁共振图像联合重建

doi:10.11772/j.issn.1001-9081.2014.11.3304

计算机应用 ›› 2014, Vol. 34 ›› Issue (11): 3304-3308.DOI: 10.11772/j.issn.1001-9081.2014.11.3304

基于群稀疏理论的乳腺动态对比度增强核磁共振图像联合重建

王冠皓,徐军

南京信息工程大学信息与控制学院,南京 210044

收稿日期:2014-05-23 修回日期:2014-06-29 出版日期:2014-11-01 发布日期:2014-12-01
通讯作者: 徐军
作者简介:
王冠皓(1989-),男,江苏徐州人,硕士研究生,主要研究方向:压缩感知、稀疏表示、深度卷积神经网络;徐军(1972-),男,江西乐平人,教授,博士,CCF会员,主要研究方向:计算机视觉、机器学习、癌症的计算机辅助检测、诊断与预后。
基金资助:
国家自然科学基金资助项目;江苏省“六大人才高峰”高层次人才项目;江苏省自然科学资助项目

Joint reconstruction of breast dynamic contrast-enhanced magnetic resonance images with group sparsity method

WANG Guanhao,XU Jun

School of Information and Control, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China

Received:2014-05-23 Revised:2014-06-29 Online:2014-11-01 Published:2014-12-01
Contact: XU Jun

摘要/Abstract

摘要：

乳腺在注射造影剂钆喷酸葡胺（Gd-DTPA）后,乳腺核磁共振（MR）图像中恶性肿瘤区域比正常或者良性区域呈现出更加快速和更强的灰度变化,因此动态对比度增强核磁共振成像（DCE-MRI）成为了医生检测和诊断乳腺恶性肿瘤的重要工具。但是DCE-MR图像的快速获取目前仍然是一个难题, 为了快速高效地获取这样的DCE-MR图像, 根据群稀疏思想和压缩感知（CS）理论,提出了一种结合变密度随机采样的共轭梯度下降方法。该方法首先使用变密度随机采样的方式从图像的局部k-空间（傅立叶系数）数据中获取采样信息,再将传统的基于l1范数的共轭梯度下降算法扩展到l2,1范数以使得改进的共轭梯度下降算法可以对多幅DCE-MR图像同时进行联合重建。实验结果表明:采样率小于40%时，改进的联合重建方法比多测量向量（MMV）算法在重建时间上减少了约30%；变密度随机采样比均匀随机采样在重建准确率上提高了约70%。

Abstract:

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) demonstrates that malignant tumors generally show faster and higher levels of enhancement than they are seen in benign or normal tissue, after an intravenous injection of the contrast agent Gd-DTPA, DCE-MRI has played important roles in diagnosis and detecting malignant tumor. However, it is still a challenge on the fast reconstruction of DCE-MR images. Based on the idea of group sparse and the theory of Compressed Sensing (CS), a conjugate gradient algorithm combined with variable density random sampling method was employed to get samples from the local k-spaces (Fourier coefficient) sampling data. Then traditional l1 norm conjugate gradient descent algorithm was extended to l2,1 norm to jointly reconstruct multiple DCE-MR images simultaneously. Compared with conventional Multi-Measurement Vector (MMV) algorithm, the proposed approach yields a faster and more accurate reconstruction result. The experimental results show that when the sampling rate is less than 40%, the joint reconstruction time based on conjugate gradient algorithm almost decreased by 30% compared with the MMV algorithm. In addition, compared with the uniform random sampling, the variable density random sampling method improves the accuracy rate about 70%.

中图分类号:

TN919.81

王冠皓徐军. 基于群稀疏理论的乳腺动态对比度增强核磁共振图像联合重建[J]. 计算机应用, 2014, 34(11): 3304-3308.

WANG Guanhao XU Jun. Joint reconstruction of breast dynamic contrast-enhanced magnetic resonance images with group sparsity method[J]. Journal of Computer Applications, 2014, 34(11): 3304-3308.

参考文献

[1]HEYWANG S H, WOLF A, PRUSS E, et al. MR imaging of the breast with Gd-DTPA: use and limitations[J]. Radiology, 1989, 171(1): 95-103.
[2]DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[3]LUSTIG M, DONOHO D, PAULY J M. Sparse MRI: the application of compressed sensing for rapid MR imaging[J]. Magnetic Resonance in Medicine, 2007, 58(6): 1182-1195.
[4]SHEIKH M A, MILENKOVIC O, BARANIUK R G. Designing compressive sensing DNA microarrays[C]// Proceedings of the 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. Piscataway: IEEE Press, 2007: 141-144.
[5]BORGNAT P, FLANDRIN P. Time-frequency localization from sparsity constraints[C]// Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE Press, 2008: 3785-3788.
[6]WILLETT R M, GEHM M E, BRADY D J. Multiscale reconstruction for computational spectral imaging[C]// Proceedings of SPIE 6498. Bellingham: SPIE Press, 2007: 64980L-1-64980L-15.
[7]MA J, Le DIMET F X. Deblurring from highly incomplete measurements for remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(3): 792-802.
[8]CANDES E J, TAO T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.
[9]BOBIN J, STARCK J L, OTTENSAMER R. Compressed sensing in astronomy[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(5): 718-726.
[10]LUSTIG M, DONOHO D L, SANTOS J M, et al. Compressed sensing MRI[J]. IEEE Signal Processing Magazine, 2008, 25(2): 72-82.
[11]STRICKLAND R, HAHN H I. Wavelet transforms for detecting microcalcifications in mammograms[J]. IEEE Transactions on Medical Imaging, 1996, 15(2): 218-229.
[12]QU X, ZHANG W, GUO D, et al. Iterative thresholding compressed sensing MRI based on contourlet transform[J]. Inverse Problems in Science and Engineering, 2010, 18(6): 737-758.
[13]MAJUMDAR A, WARD R K. Joint reconstruction of multiecho MR images using correlated sparsity[J]. Magnetic Resonance Imaging, 2011, 29(7): 899-906.
[14]HUANG J, CHEN C, AXEL L. Fast multi-contrast MRI reconstruction[EB/OL].[2013-10-10]. http://ranger.uta.edu/~huang/papers/MICCAI12_MTMRI.pdf.
[15]BILGIC B, GOYAL V K, ADALSTEINSSON E. Multi-contrast reconstruction with Bayesian compressed sensing[J]. Magnetic Resonance in Medicine, 2011, 66(6): 1601-1615.
[16]ELDAR Y C, KUPPINGER P, BOLCSKEI H. Block-sparse signals: Uncertainty relations and efficient recovery[J]. IEEE Transactions on Signal Processing, 2010, 58(6): 3042-3054.
[17]STOJNIC M, PARVARESH F, HASSIBI B. On the reconstruction of block-sparse signals with an optimal number of measurements[J]. IEEE Transactions on Signal Processing, 2009, 57(8): 3075-3085.
[18]MAJUMDAR A, WARD R K. Compressive color imaging with group-sparsity on analysis prior[C]// Proceedings of the 2010 17th IEEE International Conference on Image Processing. Piscataway: IEEE Press, 2010: 1337-1340.
[19]MAJUMDAR A, WARD R K. Non-convex group sparsity: Application to color imaging[C]// Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing. Piscataway: IEEE Press, 2010: 469-472.
[20]MAJUMDAR A, WARD R K. Face recognition from video: an MMV recovery approach[C]// Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE Press, 2012: 2221-2224.
[21]HUANG J, ZHANG T. The benefit of group sparsity[J]. The Annals of Statistics, 2010, 38(4): 1978-2004.
[22]PATI Y C, REZAIIFAR R, KRISHNAPRASAD P S. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition [C]// Proceedings of the 1993 Conference Record of the Twenty-seventh Asilomar Conference on Signals, Systems and Computers. Piscataway: IEEE Press,1993,1: 40-44.

基于群稀疏理论的乳腺动态对比度增强核磁共振图像联合重建

Joint reconstruction of breast dynamic contrast-enhanced magnetic resonance images with group sparsity method

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