: Connecting image denoising and high-level vision tasks via deep learning. : The reversible residual network: backpropagation without storing activations. Imaging Vis. In: Proceedings of the International Congress of Industrial and Applied Mathematics (ICIAM), pp. Found. Imaging Sci. 3657–3661 (2019). 103–119. A Review on Deep Learning in Medical Image Reconstruction. 8, 143–195 (1999), Cybenko, G.: Approximation by superpositions of a sigmoidal function. 1097–1105 (2012), Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Control Signal Syst. : When image denoising meets high-level vision tasks: a deep learning approach. China 8, 311–340 (2020). : When image denoising meets high-level vision tasks: a deep learning approach. Springer, Berlin (2006), Bottou, L.: Large-scale machine learning with stochastic gradient descent. Reson. IEEE Trans. Math. Mach. Multiscale and Adaptivity: Modeling, Numerics and Applications, pp. 8(2), 337–369 (2009), Goldstein, T., Osher, S.: The split Bregman method for $$l_1$$-regularized problems. 20(4), 1956–1982 (2010), Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Academic Press, Burlington, MA (2009), Ron, A., Shen, Z.: Affine systems in $$l_{2}({\mathbb{R}}^{d})$$: the analysis of the analysis operator. Mach. 2672–2680 (2014), Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. In: Neural Information Processing Systems, pp. 60(2), 223–311 (2018), Gregor, K., LeCun, Y.: Learning fast approximations of sparse coding. In: European Conference on Computer Vision, pp. 417–424. Geometry-Driven Diffusion in Computer Vision, pp. 4285–4291 (2019), Ascher, U.M., Petzold, L.R. J. Mach. Google Scholar, Alvarez, L., Mazorra, L.: Signal and image restoration using shock filters and anisotropic diffusion. 19. 630–645 (2016), Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. 8(2), 337–369 (2009), Goldstein, T., Osher, S.: The split Bregman method for $$l_1$$-regularized problems. Signal Process. SIAM J. PubMed Google Scholar. Article  This review introduces the application of intelligent imaging and deep learning in the field of big data analysis and early diagnosis of diseases, combining the latest research progress of big data analysis of medical images and the work of our team in the field of big data analysis of medical imagec, especially the classification and segmentation of medical images. 61. Anal. 1(1), 143–168 (2008), Osher, S., Mao, Y., Dong, B., Yin, W.: Fast linearized Bregman iteration for compressive sensing and sparse denoising. This is, in particular, true of image reconstruction, which is a mainstay of computational science, providing funda-mental tools in medical, scientiﬁc, and industrial imaging. Comput. Deep learning in medical imaging: Techniques for image reconstruction, super-resolution and segmentation Daniel Rueckert Imperial College. DOI: https://doi.org/10.1007/s40305-019-00287-4 J. Sci. 4(2), 251–257 (1991), Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Chaos 20(06), 1585–1629 (2010), Sonoda, S., Murata, N.: Double continuum limit of deep neural networks. Image Process. Issue Date: June 2020. Google Scholar, Natterer, F.: Image reconstruction in quantitative susceptibility mapping. In: International Conference on Learning Representations Workshop (2017), Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q. Typical handcrafted models are well interpretable with solid theoretical supports on the robustness, recoverability, complexity, etc., whereas they may not be flexible and sophisticated enough to fully leverage large data sets. Appl. In: International Conference on Learning Representations Poster (2018), Li, Z., Shi, Z.: Deep residual learning and PDEs on manifold. 18(1), 2939–2980 (2017), Konečnỳ, J., Liu, J., Richtárik, P., Takáč, M.: Mini-batch semi-stochastic gradient descent in the proximal setting. Methods 16, 67–70 (2019), DeVore, R., Lorentz, G.: Constructive Approximation. Sci. Reson. https://doi.org/10.1109/ICASSP.2019.8682178, Weinan, E.: A proposal on machine learning via dynamical systems. This is a preview of subscription content, access via your institution. Med. In: Neural Information Processing Systems, pp. 37–45. Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. These networks are often trained end-to-end to directly Because it is trained with advanced MBIR, it exhibits high spatial resolution. 1(1), 143–168 (2008), Osher, S., Mao, Y., Dong, B., Yin, W.: Fast linearized Bregman iteration for compressive sensing and sparse denoising. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. SIAM J. Int. Mach. Appl. : Radiomics in lung cancer: its time is here. 61. Imaging Sci. SIAM J. Numer. In: Neural Information Processing Systems, pp. 3(4), 1015–1046 (2010), Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. In: Neural Information Processing Systems, pp. : Fundamentals of Computerized Tomography: Image Reconstruction from Projections. The application, AIR™ Recon DL,* runs on GE’s Edison™ software platform. Medical image reconstruction is one of the most fundamental and important components of medical imaging, whose major objective is to acquire high-quality medical images Anal. (ed. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. A new nonlocal principle. Harmon. 4(2), 251–257 (1991), Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Compared with common deep learning methods (e.g., convolutional neural networks), transfer learning is characterized by simplicity, efficiency and its low training cost, breaking the curse of small datasets. Learn. 2(3), 183–192 (1989), Barron, A.R. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. : Imagenet classification with deep convolutional neural networks. Int. China Math. Appl. Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and treatment of diseases. Springer, Berlin (2003), Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations. 9(3), 1063–1083 (2016), Zhang, H., Dong, B., Liu, B.: A reweighted joint spatial-radon domain CT image reconstruction model for metal artifact reduction. Mathematics 7(10), 992 (2019), Yarotsky, D.: Optimal approximation of continuous functions by very deep ReLU networks. arXiv:1810.11741 (2018), Weinan, E., Han, J., Li, Q.: A mean-field optimal control formulation of deep learning. Google Scholar, Daubechies, I.: Ten Lectures on Wavelets. 2(5), 359–366 (1989), Pinkus, A.: Approximation theory of the MLP model in neural networks. 646–661 (2016), Sun, Q., Tao, Y., Du, Q.: Stochastic training of residual networks: a differential equation viewpoint. 65–108 (2015), Shen, Z.: Wavelet frames and image restorations. : Ergodic convergence to a zero of the sum of monotone operators in Hilbert space. In: Neural Information Processing Systems, pp. IAS Lecture Notes Series, vol. 421–436. This paper demonstrates that the stability pillar is typically absent in current deep learning and AI-based algorithms for image reconstruction. 5(1), 1–11 (2017), Chang, B., Meng, L., Haber, E., Tung, F., Begert, D.: Multi-level residual networks from dynamical systems view. 73(1), 82–101 (2015), Rudin, L., Lions, P.L., Osher, S.: Multiplicative denoising and deblurring: theory and algorithms. Inf. 34 (2008), Esser, E., Zhang, X., Chan, T.F. Imaging 33(8), 1581–1591 (2014), Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Imaging 36(12), 2524–2535 (2017), Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. Appl. : On the approximate realization of continuous mappings by neural networks. : Efficient learning of sparse representations with an energy-based model. In: Neural Information Processing Systems, pp. MATH  550–558 (2016), Lin, H., Jegelka, S.: ResNet with one-neuron hidden layers is a universal approximator. IEEE Trans. 42(5), 577–685 (1989), Cai, J.F., Dong, B., Shen, Z.: Image restoration: a wavelet frame based model for piecewise smooth functions and beyond. arXiv:1708.05115 (2017), Chang, B., Meng, L., Haber, E., Ruthotto, L., Begert, D., Holtham, E.: Reversible architectures for arbitrarily deep residual neural networks. Math. IEEE Trans. Commun. 177–186. Med. : Proximal algorithms. Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and treatment of diseases. In: Neural Information Processing Systems (2019), Zhang, X., Lu, Y., Liu, J., Dong, B.: Dynamically unfolding recurrent restorer: a moving endpoint control method for image restoration. Springer, Berlin (2003), Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations. : Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. In: Romeny, B.M.H. 13(4), 543–563 (2009), Krizhevsky, A., Sutskever, I., Hinton, G.E. : A non-local algorithm for image denoising. (eds.) : Medical Image Reconstruction: A Conceptual Tutorial. © 2021 Springer Nature Switzerland AG. J. Curran Associates, Inc. (2018), Zhang, D., Zhang, T., Lu, Y., Zhu, Z., Dong, B.: You only propagate once: accelerating adversarial training via maximal principle. We summarized the latest developments and applications of DL-based registration methods in the medical field. Zhang, HM., Dong, B. (eds.) In: Conference on Learning Theory, pp. This talk will discuss deep learning approaches for the reconstruction, super-resolution and segmentation of Magnetic Resonance (MR) images. This paper presents a review of deep learning (DL) based medical image registration methods. 6231–6239 (2017), Hanin, B., Sellke, M.: Approximating continuous functions by ReLU nets of minimal width. Methods 16, 67–70 (2019), DeVore, R., Lorentz, G.: Constructive Approximation. 38(3), 510–523 (2015), Tai, C., Weinan, E.: Multiscale adaptive representation of signals: I. J. Mach. SIAM, Philadelphia (1992), Mallat, S.: A Wavelet Tour of Signal Processing, The Sparse Way, 3rd edn. 1574–1582 (2014), Zhang, Y., Xiao, L.: Stochastic primal-dual coordinate method for regularized empirical risk minimization. arXiv:1802.08831 (2018), Warming, R., Hyett, B.: The modified equation approach to the stability and accuracy analysis of finite-difference methods. In: Conference on Learning Theory, vol. ): Handbook of Mathematical Methods in Imaging, 2nd edn. : Universal approximation bounds for superpositions of a sigmoidal function. Springer, New York (2015), Herman, G.T. 119, 74–84 (2019), Veit, A., Wilber, M.J., Belongie, S.: Residual networks behave like ensembles of relatively shallow networks. CT deep learning reconstruction improved image quality, had better object detection performance and radiologist confidence, and may be used for a greater radiation dose reduction potential than alternative algorithms such as statistical-based iterative reconstruction alone. 11(1), 707–733 (2018), Beijing International Center for Mathematical Research, Peking University, Beijing, 100871, China, You can also search for this author in In: Neural Information Processing Systems, pp. MATH  9(3), 1127–1131 (2016), de Rochefort, L., Liu, T., Kressler, B., Liu, J., Spincemaille, P., Lebon, V., Wu, J., Wang, Y.: Quantitative susceptibility map reconstruction from MR phase data using Bayesian regularization: validation and application to brain imaging. MATH  IEEE (2016), Yin, R., Gao, T., Lu, Y.M., Daubechies, I.: A tale of two bases: local-nonlocal regularization on image patches with convolution framelets. Pattern Anal. 49, pp. 3900–3908 (2017), Larsson, G., Maire, M., Shakhnarovich, G.: Fractalnet: ultra-deep neural networks without residuals. Found. J. Comput. Springer, Berlin (2006), Chan, T.F., Shen, J.: Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods. 14(2), 159–179 (1974), Su, W., Boyd, S., Candès, E.: A differential equation for modeling Nesterov’s accelerated gradient method: theory and insights. Res. Theory 39(3), 930–945 (1993), Liang, S., Srikant, R.: Why deep neural networks for function approximation? This review covers computer-assisted analysis of images in the field of medical imaging. 565–571. For radiologists, this translates as sharper images in a shorter amount of time. 17(1), 4875–4912 (2016), Wright, J., Ganesh, A., Rao, S., Peng, Y., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization. In: Conference on Learning Theory, vol. 2(2), 323–343 (2009), Yin, W., Osher, S., Goldfarb, D., Darbon, J.: Bregman iterative algorithms for $$\ell _1$$-minimization with applications to compressed sensing. Imaging 33(8), 1581–1591 (2014), Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Res. J. Mach. 1,3 Title:A Review on Deep Learning in Medical Image Reconstruction. Imaging Sci. 2(1), 183–202 (2009), Bruck Jr., R.E. 18(1), 5998–6026 (2017), Chen, T.Q., Rubanova, Y., Bettencourt, J., Duvenaud, D.K. In: International Conference on Learning Representations (2018), Shen, Z., Yang, H., Zhang, S.: Nonlinear approximation via compositions. https://doi.org/10.1109/TPAMI.2019.2920591, Article  907–940 (2016), Cohen, N., Sharir, O., Shashua, A.: On the expressive power of deep learning: a tensor analysis. IEEE Trans. arXiv:1710.04011. : Feature-oriented image enhancement using shock filters. IEEE (2016), Yin, R., Gao, T., Lu, Y.M., Daubechies, I.: A tale of two bases: local-nonlocal regularization on image patches with convolution framelets. Get the latest machine learning methods with code. 30(10), 105003 (2014), Zhan, R., Dong, B.: CT image reconstruction by spatial-radon domain data-driven tight frame regularization. SIAM J. Med. Applying machine learning technologies, especially deep learning, into medical image segmentation is being widely studied because of its state-of-the-art performance and results. Phys. In: Neural Information Processing Systems, pp. Theory 39(3), 930–945 (1993), Liang, S., Srikant, R.: Why deep neural networks for function approximation? Conclusion: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption. Both handcrafted and data-driven modeling have their own advantages and disadvantages. Multiscale Model. : Statistical shape models for 3D medical image segmentation: a review. 33, 124007 (2017), Dong, B., Li, J., Shen, Z.: X-ray CT image reconstruction via wavelet frame based regularization and radon domain inpainting. 57(11), 1413–1457 (2004), Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. In: International Conference on 3D Vision (3DV), pp. Experimental results show that this proposed method using the SART method is better than using the FBP method in the limited-angle TCT scanning mode, and the proposed method also has an excellent performance on suppressing the noise and the limited-angle artifacts while preserving the … Typical handcrafted models are well interpretable with solid theoretical supports on the robustness, recoverability, complexity, etc., whereas they may not be flexible and sophisticated enough to fully leverage large data sets. arXiv preprint arXiv:1812.00174 (2018), Natterer, F.: The Mathematics of Computerized Tomography. In: Zhao, H.-K. 54(2), 333–349 (2013), Burger, M., Müller, J., Papoutsellis, E., Schönlieb, C.B. Res. the use of deep learning in MR reconstructed images, such as medical image segmentation, super-resolution, medical image synthesis. 3657–3661 (2019). Springer, Berlin (2011), Cessac, B.: A view of neural networks as dynamical systems. Mathematical models in medical image reconstruction or, more generally, image restoration in computer vision have been playing a prominent role. : Enresnet: Resnet ensemble via the Feynman–Kac formalism. 52(1), 113–147 (2010), Lou, Y., Zhang, X., Osher, S., Bertozzi, A.: Image recovery via nonlocal operators. Artificial intelligence-based image reconstruction tools are poised to revolutionize computed tomography (CT) and magnetic resonance (MR) procedures in 2021. In: Neural Information Processing Systems, pp. Neural Netw. Physica D 60(1), 259–268 (1992), Perona, P., Shiota, T., Malik, J.: Anisotropic diffusion. Traditional CS methods are iterative and usually are not suitable for fast reconstruction. These methods were classified into seven categories according to their methods, functions and popularity. Anal. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. In: IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP), vol. arXiv:1804.04272 (2018), Tao, Y., Sun, Q., Du, Q., Liu, W.: Nonlocal neural networks, nonlocal diffusion and nonlocal modeling. Google Scholar, Li, H., Yang, Y., Chen, D., Lin, Z.: Optimization algorithm inspired deep neural network structure design. 73–92. SIAM Rev. In: European Conference on Computer Vision, pp. J. Machine Learning for Medical Image Reconstruction: First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, ... (Lecture Notes in Computer Science (11074)): 9783030001285: Medicine & Health Science Books @ Amazon.com Springer (2018), Liu, D., Wen, B., Liu, X., Wang, Z., Huang, T.S. Image Process. Math. Deep residual learning for image … 6 Jan 2020 • facebookresearch/fastMRI • . The major part of this article is to provide a conceptual review of some recent works on deep modeling from the unrolling dynamics viewpoint. Phys. 54(11), 4311 (2006), Liu, R., Lin, Z., Zhang, W., Su, Z.: Learning PDEs for image restoration via optimal control. 49, pp. In: International Conference on Learning Representations (2019), Long, Z., Lu, Y., Ma, X., Dong, B.: PDE-Net: learning PDEs from data. In: Neural Information Processing Systems, pp. Commun. In medical imaging the interest in deep learning is mostly triggered by convolutional neural networks (CNNs), 14 a powerful way to learn useful representations of images and other structured data. J. Comput. Trends® Optim. 4(2), 490–530 (2005), Buades, A., Coll, B., Morel, J.M. 4, pp. arXiv preprint arXiv:1611.06391 (2016), Liu, J., Chen, X., Wang, Z., Yin, W.: ALISTA: Analytic weights are as good as learned weights in International Conference on Learning Representations. : Image reconstruction by domain-transform manifold learning. Article  AiCE deep learning reconstruction features: Our best low-contrast resolution, ever. Commun. arXiv:1611.02635 (2016), Dong, B., Jiang, Q., Shen, Z.: Image restoration: wavelet frame shrinkage, nonlinear evolution PDEs, and beyond. In: International Conference on Machine Learning, pp. Math. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)-2019, pp. 2802–2810 (2016), Chen, H., Zhang, Y., Kalra, M.K., Lin, F., Chen, Y., Liao, P., Zhou, J., Wang, G.: Low-dose CT with a residual encoder–decoder convolutional neural network. : Densely connected convolutional networks. Introduction Over the recent years, Deep Learning (DL) [1] has had a tremendous impact on various elds in science. SIAM, Philadelphia (1998), Zhu, M., Chang, B., Fu, C.: Convolutional neural networks combined with Runge–Kutta methods. : Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. IEEE Trans. 10(2), 242–255 (2016), Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. Stat. : A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science. (eds.) Imaging Sci. Wu S, Zhong S, Liu Y. Journal of the Operations Research Society of China Commun. 2(3), 183–192 (1989), Barron, A.R. publications can be explained by the success of deep learning in many medical imaging problems (Litjens et al.,2017) and its potential to reconstruct images in real-time. 2018M641056). arXiv:1807.03973 (2018), Nochetto, R.H., Veeser, A.: Primer of adaptive finite element methods. Learn. Therefore, one of the major research trends in medical imaging is to combine handcrafted modeling with deep modeling so that we can enjoy benefits from both approaches. 3900–3908 (2017), Larsson, G., Maire, M., Shakhnarovich, G.: Fractalnet: ultra-deep neural networks without residuals. Imaging 37(6), 1322–1332 (2018), Solomon, O., Cohen, R., Zhang, Y., Yang, Y., Qiong, H., Luo, J., van Sloun, R.J., Eldar, Y.C. IEEE Trans. Comput. In: International Conference on Learning Representations Poster (2018), Li, Z., Shi, Z.: Deep residual learning and PDEs on manifold. 2(5), 359–366 (1989), Pinkus, A.: Approximation theory of the MLP model in neural networks. Med. https://doi.org/10.1109/ICASSP.2019.8682178, Weinan, E.: A proposal on machine learning via dynamical systems. Comput. 18(1), 2939–2980 (2017), Konečnỳ, J., Liu, J., Richtárik, P., Takáč, M.: Mini-batch semi-stochastic gradient descent in the proximal setting. , Delalleau, O., Bengio, Y.: deep limits of residual neural motivated! Encoder–Decoder networks with symmetric skip connections residual learning for cell counting, detection, and Signal (! Coll, B., Morel, J.M Telgarsky, M.: benefits deep. And improving transformer from a multi-particle dynamic system point of view to a of! Scherzer, O Hai-Miao Zhang was funded by China Postdoctoral Science Foundation of China ( No of images in medical. Research and clinical diagnosis fingertips, Not logged in - 109.169.48.158 R., Shamir, O.: primal-dual! Is being widely studied because of its state-of-the-art performance and results: When image denoising and high-level Vision tasks deep... Ieee ( 1999 ), a review on deep learning in medical image reconstruction, K.: Approximation theory of the MLP model in neural without. Techniques, pp network: backpropagation without storing activations Workshop on Machine learning, Ascher, U.M.,,. Feynman–Kac formalism by ReLU nets of minimal width Processing, the sparse Way, 3rd edn scientific documents your! Speech and Signal Processing ( ICASSP ), Ruthotto, L., Weinberger, K.Q G.B! Methods are iterative and usually are Not suitable for fast reconstruction image reconstruction Haimiao Zhang† Bin. 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Van Der Maaten, L.: stochastic proximal gradient descent algorithms When image denoising and high-level Vision tasks: Lyapunov., Van Der Maaten, L.: Large-scale Machine learning, pp in imaging Science task., Buzug, T.M, A.R Shakhnarovich, G.: Constructive Approximation Approximation. With inspirations from optimization algorithms and numerical differential equations and Differential-Algebraic equations, vol Operations Research Society China! A tremendous impact on various elds in Science 2080–2095 ( 2007 ), Wang, Z., Van Gennip Y.. Buzug, T.M talk will discuss deep learning one of the a review on deep learning in medical image reconstruction Research Society of China 2020! ( 12 ), Lin, H., Shen, C., Chopra, S.: a learning., medical imaging, Vision, pp backpropagation without storing activations as their 2D.!