Brain tissue microstructure parameters estimation method based on proximal gradient network_Journal of Biomedical Engineering

Authors：

 XU Yonghong , WANG Pengfei , DING Ling

Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;

Corresponding author：

XU Yonghong, Email: xyh@ysu.edu.cn

Keywords：

neurite orientation dispersion and density imaging; neural network; tissue microstructure; diffusion magnetic resonance

DOI：

10.7507/1001-5515.202004043

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Diffusion tensor imaging technology can provide information on the white matter of the brain, which can be used to explore changes in brain tissue structure, but it lacks the specific description of the microstructure information of brain tissue. The neurite orientation dispersion and density imaging make up for its shortcomings. But in order to accurately estimate the brain microstructure, a large number of diffusion gradients are needed, and the calculation is complex and time-consuming through maximum likelihood fitting. Therefore, this paper proposes a kind of microstructure parameters estimation method based on the proximal gradient network, which further avoids the classic fitting paradigm. The method can accurately estimate the parameters while reducing the number of diffusion gradients, and achieve the purpose of imaging quality better than the neurite orientation dispersion and density imaging model and accelerated microstructure imaging via convex optimization model.

Citation： XU Yonghong, WANG Pengfei, DING Ling. Brain tissue microstructure parameters estimation method based on proximal gradient network. Journal of Biomedical Engineering, 2021, 38(2): 333-341. doi: 10.7507/1001-5515.202004043 Copy

1.	Tournier J D. Diffusion MRI in the brain-theory and concepts. Prog Nucl Magn Reson Spectrosc, 2019, 112-113: 1-16.
2.	Billiet T, Vandenbulcke M, Mädler B, et al. Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiol Aging, 2015, 36(6): 2107-2121.
3.	Fu Xiuwei, Shrestha S, Sun Man, et al. Microstructural white matter alterations in mild cognitive impairment and Alzheimer’s disease. Clin Neuroradiol, 2020, 30(3): 569-579.
4.	Kansagra A P, Mabray M C, Ferriero D M, et al. Microstructural maturation of white matter tracts in encephalopathic neonates. Clin Imaging, 2016, 40(5): 1009-1013.
5.	Mastropietro A, Rizzo G, Fontana L, et al. Microstructural characterization of corticospinal tract in subacute and chronic stroke patients with distal lesions by means of advanced diffusion MRI. Neuroradiology, 2019, 61(9): 1033-1045.
6.	Basser P J. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed, 1995, 8(7/8): 333-344.
7.	Assaf Y, Basser P J. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage, 2005, 27(1): 48-58.
8.	Zhang H, Schneider T, Wheeler-Kingshott C A, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012, 61(4): 1000-1016.
9.	Daducci A, Canales-Rodríguez E J, Zhang H, et al. Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data. Neuroimage, 2015, 105: 32-44.
10.	Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
11.	Lundervold A S, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys, 2019, 29(2): 102-127.
12.	Yasaka K, Akai H, Kunimatsu A, et al. Deep learning with convolutional neural network in radiology. Jpn J Radiol, 2018, 36(4): 257-272.
13.	Golkov V, Dosovitskiy A, Sperl J, et al. q-Space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans Med Imaging, 2016, 35(5): 1344-1351.
14.	Gibbons E K, Hodgson K K, Chaudhari A S, et al. Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning. Magn Reson Med, 2019, 81(4): 2399-2411.
15.	YE C. Tissue microstructure estimation using a deep network inspired by a dictionary-based framework. Med Image Anal, 2017, 42: 288-299.
16.	Ye C. Estimation of tissue microstructure using a deep network inspired by a sparse reconstruction framework//Information Processing in Medical Imaging, 2017: 466-477.
17.	Ye C, Li X, Chen J. A deep network for tissue microstructure estimation using modified LSTM units. Med Image Anal, 2019, 55: 49-64.
18.	Sandino C M, Cheng J Y, Chen F, et al. Compressed sensing: from research to clinical practice with deep neural networks: shortening scan times for magnetic resonance imaging. IEEE Signal Process Mag, 2020, 37(1): 117-127.
19.	Justin D. Generic Methods for Optimization-Based Modeling. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012, 22: 318-326.
20.	Schmidt M, Roux N L, Bach F. Convergence rates of inexact proximal-gradient methods for convex optimization//Proceedings of the 24th International Conference on Neural Information Processing Systems, 2011: 1458-1466.
21.	Blumensath T, Davies M E. Iterative thresholding for sparse approximations. Journal of Fourier Analysis and Applications, 2008, 14(5): 629-654.
22.	Daubechies I, Defrise M, de Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun Pure Appl Math, 2004, 57(11): 1413-1457.
23.	Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn, 2011, 3(1): 1-122.
24.	Wang Zhangyang, Yang Yingzhen, Chang Shiyu, et al. Learning a deep l_∞ encoder for hashing, IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. , 2016: 2174-2180.
25.	van Essen D C, Smith S M, Barch D M, et al. The WU-Minn human connectome project: an overview. Neuroimage, 2013, 80: 62-79.
26.	Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med, 2018, 79(6): 3055-3071.
27.	Yang Yan, Sun Jian, Li Huibin, et al. ADMM-net: a deep learning approach for compressive sensing MRI. arXiv.org, 2017. arXiv: 1705.06869.
28.	Nath V, Schilling K G, Parvathaneni P, et al. Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI. Magn Reson Imaging, 2019, 62: 220-227.

1. Tournier J D. Diffusion MRI in the brain-theory and concepts. Prog Nucl Magn Reson Spectrosc, 2019, 112-113: 1-16.
2. Billiet T, Vandenbulcke M, Mädler B, et al. Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiol Aging, 2015, 36(6): 2107-2121.
3. Fu Xiuwei, Shrestha S, Sun Man, et al. Microstructural white matter alterations in mild cognitive impairment and Alzheimer’s disease. Clin Neuroradiol, 2020, 30(3): 569-579.
4. Kansagra A P, Mabray M C, Ferriero D M, et al. Microstructural maturation of white matter tracts in encephalopathic neonates. Clin Imaging, 2016, 40(5): 1009-1013.
5. Mastropietro A, Rizzo G, Fontana L, et al. Microstructural characterization of corticospinal tract in subacute and chronic stroke patients with distal lesions by means of advanced diffusion MRI. Neuroradiology, 2019, 61(9): 1033-1045.
6. Basser P J. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed, 1995, 8(7/8): 333-344.
7. Assaf Y, Basser P J. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage, 2005, 27(1): 48-58.
8. Zhang H, Schneider T, Wheeler-Kingshott C A, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012, 61(4): 1000-1016.
9. Daducci A, Canales-Rodríguez E J, Zhang H, et al. Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data. Neuroimage, 2015, 105: 32-44.
10. Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
11. Lundervold A S, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys, 2019, 29(2): 102-127.
12. Yasaka K, Akai H, Kunimatsu A, et al. Deep learning with convolutional neural network in radiology. Jpn J Radiol, 2018, 36(4): 257-272.
13. Golkov V, Dosovitskiy A, Sperl J, et al. q-Space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans Med Imaging, 2016, 35(5): 1344-1351.
14. Gibbons E K, Hodgson K K, Chaudhari A S, et al. Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning. Magn Reson Med, 2019, 81(4): 2399-2411.
15. YE C. Tissue microstructure estimation using a deep network inspired by a dictionary-based framework. Med Image Anal, 2017, 42: 288-299.
16. Ye C. Estimation of tissue microstructure using a deep network inspired by a sparse reconstruction framework//Information Processing in Medical Imaging, 2017: 466-477.
17. Ye C, Li X, Chen J. A deep network for tissue microstructure estimation using modified LSTM units. Med Image Anal, 2019, 55: 49-64.
18. Sandino C M, Cheng J Y, Chen F, et al. Compressed sensing: from research to clinical practice with deep neural networks: shortening scan times for magnetic resonance imaging. IEEE Signal Process Mag, 2020, 37(1): 117-127.
19. Justin D. Generic Methods for Optimization-Based Modeling. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012, 22: 318-326.
20. Schmidt M, Roux N L, Bach F. Convergence rates of inexact proximal-gradient methods for convex optimization//Proceedings of the 24th International Conference on Neural Information Processing Systems, 2011: 1458-1466.
21. Blumensath T, Davies M E. Iterative thresholding for sparse approximations. Journal of Fourier Analysis and Applications, 2008, 14(5): 629-654.
22. Daubechies I, Defrise M, de Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun Pure Appl Math, 2004, 57(11): 1413-1457.
23. Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn, 2011, 3(1): 1-122.
24. Wang Zhangyang, Yang Yingzhen, Chang Shiyu, et al. Learning a deep l_∞ encoder for hashing, IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. , 2016: 2174-2180.
25. van Essen D C, Smith S M, Barch D M, et al. The WU-Minn human connectome project: an overview. Neuroimage, 2013, 80: 62-79.
26. Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med, 2018, 79(6): 3055-3071.
27. Yang Yan, Sun Jian, Li Huibin, et al. ADMM-net: a deep learning approach for compressive sensing MRI. arXiv.org, 2017. arXiv: 1705.06869.
28. Nath V, Schilling K G, Parvathaneni P, et al. Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI. Magn Reson Imaging, 2019, 62: 220-227.

Journal of Biomedical Engineering

Brain tissue microstructure parameters estimation method based on proximal gradient network

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content