Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
暂无分享,去创建一个
Hongming Shan | Ge Wang | Yang Yang | Hongming Shan | Ge Wang | Yang Yang
[1] G. Uhlmann,et al. Thermoacoustic tomography with variable sound speed , 2009, 0902.1973.
[2] B. Cox,et al. Photoacoustic tomography in absorbing acoustic media using time reversal , 2010 .
[3] Muyinatu A. Lediju Bell,et al. Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning , 2018, IEEE Transactions on Medical Imaging.
[4] Yulia Hristova,et al. Time reversal in thermoacoustic tomography—an error estimate , 2008, 0812.0606.
[5] Uwe Kruger,et al. Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction , 2019, Nat. Mach. Intell..
[6] Habib Ammari,et al. Mathematical Modeling in Photoacoustic Imaging of Small Absorbers , 2010, SIAM Rev..
[7] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[8] Lihong V. Wang,et al. Universal back-projection algorithm for photoacoustic computed tomography. , 2005 .
[9] Ge Wang,et al. A Perspective on Deep Imaging , 2016, IEEE Access.
[10] Yang Yang,et al. Multiwave tomography in a closed domain: averaged sharp time reversal , 2014, 1412.8262.
[11] Otmar Scherzer,et al. A direct method for photoacoustic tomography with inhomogeneous sound speed , 2015, 1507.01741.
[12] Yang Yang,et al. Multiwave tomography with reflectors: Landweber's iteration , 2016, 1603.07045.
[13] Sebastian Acosta,et al. Multiwave imaging in an enclosure with variable wave speed , 2015, 1501.07808.
[14] Yang Yang,et al. Thermo- and Photoacoustic Tomography with Variable Speed and Planar Detectors , 2016, SIAM J. Math. Anal..
[15] C. McCollough,et al. CT dose reduction and dose management tools: overview of available options. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.
[16] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[17] Jan Kautz,et al. Loss Functions for Image Restoration With Neural Networks , 2017, IEEE Transactions on Computational Imaging.
[18] P. Kuchment,et al. Mathematics of thermoacoustic tomography , 2007, European Journal of Applied Mathematics.
[19] B T Cox,et al. k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields. , 2010, Journal of biomedical optics.
[20] Bruce R. Rosen,et al. Image reconstruction by domain-transform manifold learning , 2017, Nature.
[21] L. Landweber. An iteration formula for Fredholm integral equations of the first kind , 1951 .
[22] B T Cox,et al. Photoacoustic tomography with a single detector in a reverberant cavity. , 2009, The Journal of the Acoustical Society of America.
[23] Jeffrey A. Fessler,et al. Image Reconstruction is a New Frontier of Machine Learning , 2018, IEEE Transactions on Medical Imaging.
[24] Linh V. Nguyen,et al. Reconstruction and time reversal in thermoacoustic tomography in acoustically homogeneous and inhomogeneous media , 2008 .
[25] Guang Li,et al. CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) , 2018, IEEE Transactions on Medical Imaging.
[26] Junjie Yao,et al. Photoacoustic tomography: fundamentals, advances and prospects. , 2011, Contrast media & molecular imaging.
[27] Hongkai Zhao,et al. An Efficient Neumann Series-Based Algorithm for Thermoacoustic and Photoacoustic Tomography with Variable Sound Speed , 2011, SIAM J. Imaging Sci..
[28] Andreas Hauptmann,et al. Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography , 2017, IEEE Transactions on Medical Imaging.
[29] Stephan Antholzer,et al. Deep learning for photoacoustic tomography from sparse data , 2017, Inverse problems in science and engineering.
[30] B. Holman,et al. Gradual time reversal in thermo- and photo-acoustic tomography within a resonant cavity , 2014 .
[31] Steven Guan,et al. Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal , 2018, IEEE Journal of Biomedical and Health Informatics.
[32] Sean Holman,et al. A continuous adjoint for photo-acoustic tomography of the brain , 2018, Inverse Problems.
[33] Ashkan Javaherian,et al. Direct quantitative photoacoustic tomography for realistic acoustic media , 2018, Inverse Problems.
[34] Habib Ammari,et al. Transient Wave Imaging with Limited-View Data , 2011, SIAM J. Imaging Sci..
[35] Ge Wang,et al. Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising , 2018, IEEE Access.
[36] Linh V. Nguyen,et al. A Dissipative Time Reversal Technique for Photoacoustic Tomography in a Cavity , 2015, SIAM J. Imaging Sci..
[37] Hongming Shan,et al. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network , 2018, IEEE Transactions on Medical Imaging.
[38] Lihong V. Wang,et al. Photoacoustic tomography: principles and advances. , 2016, Electromagnetic waves.
[39] Plamen Stefanov,et al. Thermoacoustic tomography arising in brain imaging , 2010, 1009.1687.