Browse > Article
http://dx.doi.org/10.13104/imri.2021.25.4.300

DEMO: Deep MR Parametric Mapping with Unsupervised Multi-Tasking Framework  

Cheng, Jing (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences)
Liu, Yuanyuan (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences)
Zhu, Yanjie (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences)
Liang, Dong (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences)
Publication Information
Investigative Magnetic Resonance Imaging / v.25, no.4, 2021 , pp. 300-312 More about this Journal
Abstract
Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.
Keywords
Compressed sensing; Fast MR imaging; Parameter mapping; Unsupervised learning;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Eo T, Jun Y, Kim T, Jang J, Lee HJ, Hwang D. KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magn Reson Med 2018;80:2188-2201   DOI
2 Aggarwal HK, Mani MP, Jacob M. MoDL: model-based deep learning architecture for inverse problems. IEEE Trans Med Imaging 2019;38:394-405   DOI
3 Adler J, Oktem O. Learned primal-dual reconstruction. IEEE Trans Med Imaging 2018;37:1322-1332   DOI
4 Cohen O, Zhu B, Rosen MS. MR fingerprinting Deep RecOnstruction NEtwork (DRONE). Magn Reson Med 2018;80:885-894   DOI
5 Yang J, Zhang Y, Yin W. A fast alternating direction method for TVL1-L2 signal reconstruction from partial Fourier data. IEEE J Sel Top Signal Process 2010;4:288-297   DOI
6 Cheng J, Ke Z, Wang H, et al. Learning reconstruction without ground-truth data: an unsupervised way for fast MR imaging. In Proceedings of the 28th Annual Meeting of ISMRM, 2020:3634
7 Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007;58:1182-1195   DOI
8 Baboli R, Sharafi A, Chang G, Regatte RR. Isotropic morphometry and multicomponent T1 rho mapping of human knee articular cartilage in vivo at 3T. J Magn Reson Imaging 2018;48:1707-1716   DOI
9 Liang D, Liu B, Wang J, Ying L. Accelerating SENSE using compressed sensing. Magn Reson Med 2009;62:1574-1584   DOI
10 Liang D, Cheng J, Ke Z, Ying L. Deep magnetic resonance image reconstruction: inverse problems meet neural networks. IEEE Signal Process Mag 2020;37:141-151   DOI
11 Sharafi A, Xia D, Chang G, Regatte RR. Biexponential T1rho relaxation mapping of human knee cartilage in vivo at 3 T. NMR Biomed 2017;30
12 Pandit P, Rivoire J, King K, Li X. Accelerated T1rho acquisition for knee cartilage quantification using compressed sensing and data-driven parallel imaging: a feasibility study. Magn Reson Med 2016;75:1256-1261   DOI
13 Zhu Y, Liu Y, Ying L, Liu X, Zheng H, Liang D. Bio-SCOPE: fast biexponential T1rho mapping of the brain using signal-compensated low-rank plus sparse matrix decomposition. Magn Reson Med 2020;83:2092-2106   DOI
14 Zhu Y, Liu Y, Ying L, et al. SCOPE: signal compensation for low-rank plus sparse matrix decomposition for fast parameter mapping. Phys Med Biol 2018;63:185009   DOI
15 Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature 2018;555:487-492   DOI
16 Han Y, Yoo J, Kim HH, Shin HJ, Sung K, Ye JC. Deep learning with domain adaptation for accelerated projection-reconstruction MR. Magn Reson Med 2018;80:1189-1205   DOI
17 Petzschner FH, Ponce IP, Blaimer M, Jakob PM, Breuer FA. Fast MR parameter mapping using k-t principal component analysis. Magn Reson Med 2011;66:706-716   DOI
18 Zhao B, Lu W, Hitchens TK, Lam F, Ho C, Liang ZP. Accelerated MR parameter mapping with low-rank and sparsity constraints. Magn Reson Med 2015;74:489-498   DOI
19 Tran-Gia J, Stab D, Wech T, Hahn D, Kostler H. Model-based acceleration of parameter mapping (MAP) for saturation prepared radially acquired data. Magn Reson Med 2013;70:1524-1534   DOI
20 Wang S, Ke Z, Cheng H, et al. DIMENSION: dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network training. NMR Biomed 2019:e4131
21 Yang Y, Sun J, Li H, Xu Z. ADMM-CSNet: a deep learning approach for image compressive sensing. IEEE Trans Pattern Anal Mach Intell 2020;42:521-538   DOI
22 Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 2018;79:3055-3071   DOI
23 Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D. A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging 2018;37:491-503   DOI
24 Cai C, Wang C, Zeng Y, et al. Single-shot T2 mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network. Magn Reson Med 2018;80:2202-2214   DOI
25 Lingala SG, Hu Y, DiBella E, Jacob M. Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Trans Med Imaging 2011;30:1042-1054   DOI
26 Lustig M, Donoho DL, Santos JM, Pauly JM. Compressed sensing MRI. IEEE Signal Process Mag 2008;25:72-82   DOI
27 Cheng J, Wang H, Ying L. Model learning: primal dual networks for fast MR imaging. Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 22nd International Conference, Shenzhen, China, 2019:21-29
28 Zhu Y, Peng X, Wu Y, et al. Direct diffusion tensor estimation using a model-based method with spatial and parametric constraints. Med Phys 2017;44:570-580   DOI
29 Peng X, Ying L, Liu Y, Yuan J, Liu X, Liang D. Accelerated exponential parameterization of T2 relaxation with model-driven low rank and sparsity priors (MORASA). Magn Reson Med 2016;76:1865-1878   DOI
30 Sibley CT, Noureldin RA, Gai N, et al. T1 Mapping in cardiomyopathy at cardiac MR: comparison with endomyocardial biopsy. Radiology 2012;265:724-732   DOI
31 Velikina JV, Alexander AL, Samsonov A. Accelerating MR parameter mapping using sparsity-promoting regularization in parametric dimension. Magn Reson Med 2013;70:1263-1273   DOI
32 Pedersen H, Kozerke S, Ringgaard S, Nehrke K, Kim WY. k-t PCA: temporally constrained k-t BLAST reconstruction using principal component analysis. Magn Reson Med 2009;62:706-716   DOI
33 Quan TM, Nguyen-Duc T, Jeong WK. Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans Med Imaging 2018;37:1488-1497   DOI
34 MacKay A, Whittall K, Adler J, Li D, Paty D, Graeb D. In vivo visualization of myelin water in brain by magnetic resonance. Magn Reson Med 1994;31:673-677   DOI
35 Duvvuri U, Charagundla SR, Kudchodkar SB, et al. Human knee: in vivo T1(rho)-weighted MR imaging at 1.5 T-- preliminary experience. Radiology 2001;220:822-826   DOI
36 Regatte RR, Akella SV, Lonner JH, Kneeland JB, Reddy R. T1rho relaxation mapping in human osteoarthritis (OA) cartilage: comparison of T1rho with T2. J Magn Reson Imaging 2006;23:547-553   DOI
37 Zhou Y, Pandit P, Pedoia V, et al. Accelerating T1rho cartilage imaging using compressed sensing with iterative locally adapted support detection and JSENSE. Magn Reson Med 2016;75:1617-1629   DOI
38 Sumpf TJ, Uecker M, Boretius S, Frahm J. Model-based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin-echo MRI. J Magn Reson Imaging 2011;34:420-428   DOI
39 Zhang T, Pauly JM, Levesque IR. Accelerating parameter mapping with a locally low rank constraint. Magn Reson Med 2015;73:655-661   DOI
40 Block KT, Uecker M, Frahm J. Model-based iterative reconstruction for radial fast spin-echo MRI. IEEE Trans Med Imaging 2009;28:1759-1769   DOI
41 Chu ML, Chang HC, Oshio K, Chen NK. A single-shot T2 mapping protocol based on echo-split gradient-spin-echo acquisition and parametric multiplexed sensitivity encoding based on projection onto convex sets reconstruction. Magn Reson Med 2018;79:383-393   DOI
42 Mardani M, Gong E, Cheng JY, et al. Deep generative adversarial neural networks for compressive sensing MRI. IEEE Trans Med Imaging 2019;38:167-179   DOI
43 Lee D, Yoo J, Tak S, Ye JC. Deep residual learning for accelerated MRI using magnitude and phase networks. IEEE Trans Biomed Eng 2018;65:1985-1995   DOI
44 Welsh CL, Dibella EV, Adluru G, Hsu EW. Model-based reconstruction of undersampled diffusion tensor k-space data. Magn Reson Med 2013;70:429-440   DOI
45 Walsh DO, Gmitro AF, Marcellin MW. Adaptive reconstruction of phased array MR imagery. Magn Reson Med 2000;43:682-690   DOI
46 Qin C, Schlemper J, Caballero J, Price AN, Hajnal JV, Rueckert D. Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging 2019;38:280-290   DOI
47 Liu F, Feng L, Kijowski R. MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR parameter mapping. Magn Reson Med 2019;82:174-188   DOI