Model-based deep learning reconstruction methods for fast magnetic resonance imaging

전요한 2022년
논문상세정보
' Model-based deep learning reconstruction methods for fast magnetic resonance imaging' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • acceleration
  • deep learning
  • magnetic resonance imaging
  • modelbased
  • reconstruction
  • 고속화
  • 딥 러닝
  • 모델기반
  • 자기공명 영상
  • 재구성
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
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' Model-based deep learning reconstruction methods for fast magnetic resonance imaging' 의 참고문헌

  • net : cross ? domain convolutional neural networks for reconstructing undersampled magnetic resonance images .
    Eo , T. KIKI ? 80 ( 5 ) : p. 2188-2201 . [2018]
  • VS-Net : Variable splitting network for accelerated parallel MRI reconstruction .
  • Time-of-flight magnetic resonance angiography with sparse undersampling and iterative reconstruction : comparison with conventional parallel imaging for accelerated imaging
    Yamamoto , T. 51 ( 6 ) : p. 372-378 . [2016]
  • T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing .
    Huang , C. 67 ( 5 ) : p. 1355-1366 . [2012]
  • Second order total generalized variation ( TGV ) for MRI .
    Knoll , F. 65 ( 2 ) : p. 480-491 . [2011]
  • Retrospective correction of motion ? affected MR images using deep learning frameworks .
    K ? stner , T. 82 ( 4 ) : p. 1527-1540 . [2019]
  • Recommendations towards standards for quantitative MRI ( qMRI ) and outstanding needs .
    Keenan , K.E. 49 ( 7 ) : p. e26 . [2019]
  • Rapid T1 quantification from high resolution 3D data with model ? based reconstruction .
    Maier , O. 81 ( 3 ) : p. 2072-2089 . [2019]
  • Quantitative susceptibility mapping using deep neural network : QSMnet
    Yoon , J. , 179 : p. 199-206 . [2018]
  • Quantitative mapping of T1 and T2 * discloses nigral and brainstem pathology in early Parkinson 's disease
    Baudrexel , S. 51 ( 2 ) : p. 512-520 . [2010]
  • Quantitative T1 and T2 mapping in recurrent glioblastomas under bevacizumab : earlier detection of tumor progression compared to conventional MRI .
    Lescher , S. 57 ( 1 ) : p. 11-20 . [2015]
  • Principles , techniques , and applications of T2 * -based MR imaging and its special applications
    Chavhan , G.B. 29 ( 5 ) : p. 1433-1449 . [2009]
  • Person re-identification by multi-channel parts-based cnn with improved triplet loss function
  • Parallel imaging in time ? of ? flight magnetic resonance angiography using deep multistream convolutional neural networks .
    Jun , Y. 81 ( 6 ) : p. 3840-3853 . [2019]
  • On the accuracy of T1 mapping : searching for common ground .
    Stikov , N. 73 ( 2 ) : p. 514-522 . [2015]
  • Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH cardiovascular magnetic resonance .
    Wang , X. 21 ( 1 ) : p. 1-11 . [2019]
  • Magnetic resonance angiography
    Graves , M. , 70 ( 829 ) : p. 6-28 . [1997]
  • MR angiography at 3 Tesla to assess proximal internal carotid artery stenoses : contrast-enhanced or 3D time-of-flight MR angiography ?
    Weber , J. , 25 ( 1 ) : p. 41-48 . [2015]
  • Learning deep CNN denoiser prior for image restoration
  • Learning a variational network for reconstruction of accelerated MRI data .
    Hammernik , K. 79 ( 6 ) : p. 3055-3071 . [2018]
  • Intracranial vascular stenosis and occlusion : comparison of 3D time-of-flight and 3D phase-contrast MR angiography
    Oelerich , M. , 40 ( 9 ) : p. 567-573 . [1998]
  • Improved image quality of intracranial aneurysms : 3.0- T versus 1.5-T time-of-flight MR angiography
    Gibbs , G.F. , 25 ( 1 ) : p. 84-87 . [2004]
  • Image reconstruction by regularized nonlinear inversion ? joint estimation of coil sensitivities and image content .
    Uecker , M. 60 ( 3 ) : p. 674-682 . [2008]
  • Image reconstruction by domain-transform manifold learning
    Zhu , B. , 555 ( 7697 ) : p. 487-492 . [2018]
  • Image quality assessment : from error visibility to structural similarity .
    Wang , Z. 13 ( 4 ) : p. 600-612 . [2004]
  • Image inpainting for irregular holes using partial convolutions .
  • HYDRA : Hybrid deep magnetic resonance fingerprinting
    Song , P. 46 ( 11 ) : p. 4951-4969 . [2019]
  • Generalized autocalibrating partially parallel acquisitions ( GRAPPA )
    Griswold , M.A. , 47 ( 6 ) : p. 1202-1210 . [2002]
  • Fast l1-SPIRiT compressed sensing parallel imaging MRI : scalable parallel implementation and clinically feasible runtime
    Murphy , M. , 31 ( 6 ) : p. 1250-1262 . [2012]
  • Fast T2 mapping with improved accuracy using undersampled spin-echo MRI and model-based reconstructions with a generating function .
    Sumpf , T.J. 33 ( 12 ) : p. 2213-2222 . [2014]
  • Exploring linearity of deep neural network trained QSM : QSMnet+ .
    Jung , W. , 211 : p. 116619 . [2020]
  • Enhanced deep residual networks for single image superresolution .
  • Dynamic and static magnetic resonance angiography of the supra-aortic vessels at 3.0 T : intraindividual comparison of gadobutrol , gadobenate dimeglumine , and gadoterate meglumine at equimolar dose .
    Kramer , J.H. , 48 ( 3 ) : p. 121-128 . [2013]
  • Delving deep into rectifiers : Surpassing human-level performance on imagenet classification
  • Deep residual learning for image recognition
  • Deep residual learning for accelerated MRI using magnitude and phase networks .
    Lee , D. 65 ( 9 ) : p. 1985-1995 . [2018]
  • Deep generative adversarial neural networks for compressive sensing MRI .
    Mardani , M. 38 ( 1 ) : p. 167-179 . [2018]
  • DAGAN : Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction .
    Yang , G. 37 ( 6 ) : p. 1310-1321 . [2017]
  • Compressed sensing MRI .
    Lustig , M. 25 ( 2 ) : p. 72-82 . [2008]
  • Compressed sensing 3-dimensional time-of-flight magnetic resonance angiography for cerebral aneurysms : optimization and evaluation .
    Fushimi , Y. 51 ( 4 ) : p. 228-235 . [2016]
  • Coil compression for accelerated imaging with Cartesian sampling .
    Zhang , T. 69 ( 2 ) : p. 571-582 . [2013]
  • Clinical performance of contrast enhanced abdominal pediatric MRI with fast combined parallel imaging compressed sensing reconstruction .
    Zhang , T. 40 ( 1 ) : p. 13- 25 . [2014]
  • Calibrationless parallel imaging reconstruction based on structured low ? rank matrix completion
    Shin , P.J. 72 ( 4 ) : p. 959-970 . [2014]
  • Beyond a gaussian denoiser : Residual learning of deep cnn for image denoising .
    Zhang , K. 26 ( 7 ) : p. 3142-3155 . [2017]
  • Aggregated residual transformations for deep neural networks
  • Accelerating Cartesian MRI by domain-transform manifold learning in phase-encoding direction .
    Eo , T. 63 : p. 101689 . [2020]
  • Accelerated MR parameter mapping with low ? rank and sparsity constraints .
    Zhao , B. 74 ( 2 ) : p. 489- 498 . [2015]
  • ADMM-CSNet : A deep learning approach for image compressive sensing .
    Yang , Y. 42 ( 3 ) : p. 521-538 . [2018]
  • A deep cascade of convolutional neural networks for dynamic MR image reconstruction .
    Schlemper , J. 37 ( 2 ) : p. 491-503 . [2017]
  • 99. Haskell, M.W., et al., Network accelerated motion estimation and reduction (NAMER): convolutional neural network guided retrospective motion correction using a separable motion model. Magnetic Resonance in Medicine, 2019. 82(4): p. 1452-1461.
    [2019]
  • 98. Jeelani, H., et al. A myocardial t1-mapping framework with recurrent and U-Net convolutional neural networks. in Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020.
    [2020]
  • 94. Balsiger, F., et al. Magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks. in Proceedings of the International Workshop on Machine Learning for Medical Image Reconstruction. 2018.
    [2018]
  • 93. Virtue, P., X.Y. Stella, and M. Lustig. Better than real: Complex-valued neural nets for MRI fingerprinting. in Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP). 2017.
    [2017]
  • 92. Hoppe, E., et al., Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series. GMDS, 2017. 243: p. 202-206.
    [2017]
  • 91. Goldfarb, J.W., J. Craft, and J.J. Cao, Water–fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network. Journal of Magnetic Resonance Imaging, 2019. 50(2): p. 655-665.
    [2019]
  • 90. Luu, H.M., et al., qMTNet: accelerated quantitative magnetization transfer imaging with artificial neural networks. Magnetic Resonance in Medicine, 2021. 85(1): p. 298-308.
  • 89. Ma, D., et al., Magnetic resonance fingerprinting. Nature, 2013. 495(7440): p. 187-192.
    [2013]
  • 83. Homer, J. and M.S. Beevers, Driven-equilibrium single-pulse observation of T1 relaxation. A reevaluation of a rapid “new” method for determining NMR spin-lattice relaxation times. Journal of Magnetic Resonance (1969), 1985. 63(2): p. 287-297.
    [1985]
  • 82. Deoni, S.C., B.K. Rutt, and T.M. Peters, Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state. Magnetic Resonance in Medicine, 2003. 49(3): p. 515-526.
    [2003]
  • 80. Ben‐Eliezer, N., D.K. Sodickson, and K.T. Block, Rapid and accurate T2 mapping from multi–spin‐echo data using Bloch‐simulation‐based reconstruction. Magnetic Resonance in Medicine, 2015. 73(2): p. 809-817.
    [2015]
  • 8. Heidemann, R.M., et al., VD‐AUTO‐SMASH imaging. Magnetic Resonance in Medicine, 2001. 45(6): p. 1066-1074.
    [2001]
  • 78. Jun, Y., et al., Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method. Medical Image Analysis, 2021. 70: p. 102017.
  • 77. Zbontar, J., et al., fastMRI: An open dataset and benchmarks for accelerated MRI. arXiv preprint arXiv:1811.08839, 2018.
    [2018]
  • 76. Paszke, A., et al., Pytorch: An imperative style, high-performance deep learning library. in Proceedings of the Advances in Neural Information Processing Systems, 2019. 32: p. 8026-8037.
    [2019]
  • 75. Ulyanov, D., A. Vedaldi, and V. Lempitsky, Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022, 2016.
    [2016]
  • 74. She, H., et al., Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing. Magnetic Resonance in Medicine, 2014. 71(2): p. 645-660.
    [2014]
  • 71. Diamond, S., et al., Unrolled optimization with deep priors. arXiv preprint arXiv:1705.08041, 2017.
    [2017]
  • 70. Block, K.T., M. Uecker, and J. Frahm, Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint. Magnetic Resonance in Medicine, 2007. 57(6): p. 1086-1098.
    [2007]
  • 7. Jakob, P.M., et al., AUTO-SMASH: a self-calibrating technique for SMASH imaging. Magnetic Resonance Materials in Physics, Biology and Medicine, 1998. 7(1): p. 42-54.
    [1998]
  • 69. Sriram, A., et al. End-to-end variational networks for accelerated MRI reconstruction. in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 2020.
    [2020]
  • 67. Ying, L. and J. Sheng, Joint image reconstruction and sensitivity estimation in SENSE (JSENSE). Magnetic Resonance in Medicine, 2007. 57(6): p. 1196-1202.
    [2007]
  • 66. Jun, Y., et al. Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
  • 65. Szegedy, C., et al. Inception-v4, inception-resnet and the impact of residual connections on learning. in Proceedings of the Thirty-first AAAI Conference on Artificial Intelligence. 2017.
    [2017]
  • 61. McDonald, J.H., Handbook of biological statistics. Vol. 2. 2009: sparky house publishing Baltimore, MD.
    [2009]
  • 6. Bydder, M., D.J. Larkman, and J.V. Hajnal, Generalized SMASH imaging. Magnetic Resonance in Medicine, 2002. 47(1): p. 160-170.
    [2002]
  • 57. Ronneberger, O., P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 2015.
    [2015]
  • 56. Vasanawala, S., et al. Practical parallel imaging compressed sensing MRI: Summary of two years of experience in accelerating body MRI of pediatric patients. in Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2011.
    [2011]
  • 55. Uecker, M., et al., ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magnetic Resonance in Medicine, 2014. 71(3): p. 990-1001.
    [2014]
  • 54. Abadi, M., et al. Tensorflow: A system for large-scale machine learning. in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). 2016.
    [2016]
  • 53. Kingma, D.P. and J. Ba, Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
    [2014]
  • 50. Kim, J., J.K. Lee, and K.M. Lee. Accurate image super-resolution using very deep convolutional networks. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
    [2016]
  • 5. Sodickson, D.K. and W.J. Manning, Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magnetic Resonance in Medicine, 1997. 38(4): p. 591-603.
    [1997]
  • 49. Ioffe, S. and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. in Proceedings of the International Conference on Machine Learning. 2015.
    [2015]
  • 47. Eitel, A., et al. Multimodal deep learning for robust RGB-D object recognition. in Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2015.
    [2015]
  • 44. Wilson, G.J., et al., Parallel imaging in MR angiography. Topics in Magnetic Resonance Imaging, 2004. 15(3): p. 169-185.
    [2004]
  • 41. White, P.M., et al., Intracranial aneurysms: CT angiography and MR angiography for detection—prospective blinded comparison in a large patient cohort. Radiology, 2001. 219(3): p. 739-749.
    [2001]
  • 40. White, P.M., J.M. Wardlaw, and V. Easton, Can noninvasive imaging accurately depict intracranial aneurysms? A systematic review. Radiology, 2000. 217(2): p. 361-370.
    [2000]
  • 4. Pruessmann, K.P., et al., SENSE: sensitivity encoding for fast MRI. Magnetic Resonance in Medicine, 1999. 42(5): p. 952-962.
    [1999]
  • 38. Dagirmanjian, A., et al., High resolution, magnetization transfer saturation, variable flip angle, time-of-flight MRA in the detection of intracranial vascular stenoses. Journal of Computer Assisted Tomography, 1995. 19(5): p. 700-706.
    [1995]
  • 36. Quan, T.M., T. Nguyen-Duc, and W.-K. Jeong, Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Transactions on Medical Imaging, 2018. 37(6): p. 1488-1497.
    [2018]
  • 31. Mardani, M., et al., Neural proximal gradient descent for compressive imaging. arXiv preprint arXiv:1806.03963, 2018.
    [2018]
  • 3. Nishimura, D.G., Principles of magnetic resonance imaging. 1996: Stanford University.
    [1996]
  • 29. Aggarwal, H.K., M.P. Mani, and M. Jacob, MoDL: Model-based deep learning architecture for inverse problems. IEEE Transactions on Medical Imaging, 2018. 38(2): p. 394-405.
    [2018]
  • 25. Han, Y., L. Sunwoo, and J.C. Ye, k-Space Deep Learning for Accelerated MRI. IEEE Transactions on Medical Imaging, 2019. 39(2): p. 377-386.
    [2019]
  • 24. Akçakaya, M., et al., Scan‐specific robust artificial‐neural‐networks for kspace interpolation (RAKI) reconstruction: Database‐free deep learning for fast imaging. Magnetic Resonance in Medicine, 2019. 81(1): p. 439-453.
    [2019]
  • 22. Kwon, K., D. Kim, and H. Park, A parallel MR imaging method using multilayer perceptron. Medical Physics, 2017. 44(12): p. 6209-6224.
    [2017]
  • 20. Kulkarni, K., et al. Reconnet: Non-iterative reconstruction of images from compressively sensed measurements. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
    [2016]
  • 2. Matcuk, G.R., J.S. Gross, and J. Fritz, Compressed Sensing MRI: Technique and Clinical Applications. Advances in Clinical Radiology, 2020. 2: p. 257- 271.
    [2020]
  • 16. Chen, C. and J. Huang, Compressive sensing MRI with wavelet tree sparsity. in Proceedings of the Advances in Neural Information Processing Systems, 2012. 25: p. 1115-1123.
    [2012]
  • 15. Haldar, J.P. and J. Zhuo, P‐LORAKS: low‐rank modeling of local k‐space neighborhoods with parallel imaging data. Magnetic Resonance in Medicine, 2016. 75(4): p. 1499-1514.
    [2016]
  • 14. Haldar, J.P., Low-rank modeling of local k-space neighborhoods (LORAKS) for constrained MRI. IEEE Transactions on Medical Imaging, 2013. 33(3): p. 668-681.
    [2013]
  • 111. Vrenken, H., et al., Whole-brain T1 mapping in multiple sclerosis: global changes of normal-appearing gray and white matter. Radiology, 2006. 240(3): p. 811-820.
    [2006]
  • 11. Lustig, M., D. Donoho, and J.M. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine, 2007. 58(6): p. 1182-1195.
    [2007]
  • 108. Zibetti, M.V., et al., Accelerating 3D‐T1ρ mapping of cartilage using compressed sensing with different sparse and low rank models. Magnetic Resonance in Medicine, 2018. 80(4): p. 1475-1491.
    [2018]
  • 107. Liu, F., L. Feng, and R. Kijowski, MANTIS: Model‐Augmented Neural neTwork with Incoherent k‐space Sampling for efficient MR parameter mapping. Magnetic Resonance in Medicine, 2019. 82(1): p. 174-188.
    [2019]
  • 106. Zhang, T., J.M. Pauly, and I.R. Levesque, Accelerating parameter mapping with a locally low rank constraint. Magnetic Resonance in Medicine, 2015. 73(2): p. 655-661.
    [2015]
  • 105. Liberman, G., Y. Louzoun, and D. Ben Bashat, T1 mapping using variable flip angle SPGR data with flip angle correction. Journal of Magnetic Resonance Imaging, 2014. 40(1): p. 171-180.
    [2014]
  • 103. Block, K.T., M. Uecker, and J. Frahm, Model-based iterative reconstruction for radial fast spin-echo MRI. IEEE Transactions on Medical Imaging, 2009. 28(11): p. 1759-1769.
    [2009]
  • 102. Lankford, C.L., R.D. Dortch, and M.D. Does, Fast T2 mapping with multiple echo, Caesar cipher acquisition and model‐based reconstruction. Magnetic Resonance in Medicine, 2015. 73(3): p. 1065-1074.
    [2015]
  • 100. Johnson, P.M. and M. Drangova, Conditional generative adversarial network for 3D rigid‐body motion correction in MRI. Magnetic Resonance in Medicine, 2019. 82(3): p. 901-910.
    [2019]
  • 10. Donoho, D.L., Compressed sensing. IEEE Transactions on Information Theory, 2006. 52(4): p. 1289-1306.
    [2006]