MRI图像的压缩感知重建算法开题报告
2022-01-13 21:38:38
全文总字数:2973字
1. 研究目的与意义及国内外研究现状
目的:针对mri磁共振成像,介绍传统的mri压缩感知重建算法,以及基于深度学习的mri压缩感知重建算法,实现更快构建更高质量的mr图像,使图像达到临床应用的高标准。
意义:随着磁共振技术的迅速发展,经典的磁共振图像重建在重建速度以及重建质量上很难有进一步的突破,而高质量与快速率的图像重建对临床医学越来越重要。本论文通过构建用于mri图像压缩感知重建的深度网络,验证了重建算法的有效性,具有一定的理论意义和实际应用价值。
2. 研究的基本内容
研究内容:综述mri压缩感知传统的重建算法,以及基于深度学习的重建算法,并建立深度学习网络模型,验证重建算法的有效性,具有一定的理论意义和实际应用价值。
主要包括:
(1)阐述mri成像的基本原理,分析其存在的成像时间长等问题;
3. 实施方案、进度安排及预期效果
实施方案:通过研究相关资料,了解mri磁共振成像的基本原理,掌握传统的mri压缩感知重建算法和基于深度学习的mri压缩感知重建方法。在研究过程中,构建深度网络模型,与理论研究不断交互,互相验证,最终完成本次毕业论文。
进度:
4. 参考文献
[1]王水花,张煜东.压缩感知磁共振成像技术综述.CHINESE JOURNAL OF MEDICAL PHYSICS, 2015, 32(2):1005-202X. [2] S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang. Accelerating magnetic resonance imaging via deep learning, in ISBI, 2016. [3] Yang G , Yu S , Dong H , et al. DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction[J].IEEE Transactions on Medical Imaging, 2018:1-1. [4] K. G. Hollingsworth, “Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction,” Phys. Med.Biol., vol. 60, no. 21, pp. 297–322, 2015. [5] M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE Signal Process. Mag., vol. 25, no. 2, pp. 72–82,2008. [6] D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol.52, no. 4, pp. 1289–1306, 2006. [7] M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: the application of compressed sensing for rapid MR imaging,” Magn. Reson. Med., vol.58, no. 6, pp. 1182–1195, 2007. [8] K. T. Block, M. Uecker, and J. Frahm, “Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint.” Magn. Reson. Med., vol. 57, no. 6, pp. 1086–1098, 2007. [9] Z. Lai, X. Qu, Y. Liu, D. Guo, J. Ye, Z. Zhan, and Z. Chen, “Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform,” Med. Image Anal., vol. 27, pp. 93–104, 2016. [10] . Zhan, J. Cai, D. Guo, Y. Liu, Z. Chen, and X. Qu, “Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction,”IEEE Trans. Biomed. Eng., vol. 63, no. 9, pp. 1850–1861, 2016. [11] Z. Zhu, K. Wahid, P. Babyn, and R. Yang, “Compressed sensing-based MRI reconstruction using complex double-density dual-tree DWT,” Int.J. Biomed. Imaging, vol. 2013, pp. 10, 2013. |