References on Geometric Tomography and Related Topics

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References on Geometric and Microlocal Analysis

16 September 2022

Integral Geometry and Geometric Tomography

Analytic microlocal analysis and tomography
- A. Homan and H. Zhou, Injectivity and stability for a generic class of generalized Radon transforms, The Journal of Geometric Analysis, 27 (2017), pp.1515–1529, doi.
- P. Stefanov, Support theorems for the light ray transform on analytic Lorentzian manifolds, Proceeding of the American Mathematical Society, 145 (2017), pp.1259-1274, doi.
- E. T. Quinto, Radon transforms satisfying the Bolker assumption, pp. 263-270, Proceedings of conference "Seventy-five Years of Radon Transforms", International Press Co. Ltd., Hong Kong, 1994.
- E. T. Quinto, Support theorems for the spherical Radon transform on manifolds, International Mathematics Research Notices, Volume 2006 (2006), article ID 67205, doi.
Microlocal Artifacts
- L. Borg, J. Frikel, J. S. Jørgensen and E. T. Quinto, Analyzing reconstruction artifacts from arbitrary incomplete X-ray CT data, SIAM Journal on Imaging Sciences, 11 (2018), pp.2786--2814, doi.
- J. K. Choi, H. S. Park, S. Wang, Y. Wang, and J. K. Seo, Inverse problem in quantitative susceptibility mapping, SIAM Jornal of Imaging Science, 7 (2014), pp.1669–1689, doi.
- H. S. Park, J. K. Choi, and J. K. Seo, Characterization of metal artifacts in X-ray computed tomography, Communications on Pure and Applied Mathematics, 70 (2017), pp.2191–2217, doi.
- B. Palacios, G. Uhlmann and Y. Wang, Reducing streaking artifacts in quantitative susceptibility mapping, SIAM Journal on Imaging Science, 10 (2017), pp.1921--1934, doi.
- B. Palacios, G. Uhlmann and Y. Wang, Quantitative analysis of metal artifacts in X-ray tomography, SIAM Journal on Mathematical Analysis, 50 (2018), pp.4914--4936, doi.
- Y. Wang and Y. Zou, Streak artifacts from non-convex metal objects in X-ray tomography, Pure and Applied Analysis, 3 (2021), pp.295-318, doi.
- E. T. Quinto, An introduction to X-ray tomography and Radon transforms, "The Radon Transform, Inverse Problems, and Tomography", Proceedings of Symposia in Applied Mathematics, 63, pp.1--23, American Mathematical Society, Providence, RI, 2006, url.
- V. P. Krishnan and E. T. Quinto, Microlocal analysis in tomography, Handbook of mathematical methods in imaging. Vol. 1, 2, 3, pp.847--902, Springer, New York, 2015, pdf.
Spiral CT
- A. Katsevich, Theoretically exact filtered backprojection-type inversion algorithm for spiral CT, SIAM Journal of Applied Mathematics, 62 (2002), pp.2012–2026, doi.
- A. Katsevich, Microlocal analysis of an FBP algorithm for truncated spiral cone beam data, Journal of Fourier Analysis and Applications, 8 (2002), pp.407–425, doi.
- A. Katsevich, An improved exact filtered backprojection algorithm for spiral computed tomography, Advances in Applied Mathematics 32 (2004), pp.681–697, doi.
- A. Katsevich, Stability estimates for helical computer tomography, Journal of Fourier Analysis and Applications, 11 (2005), pp.85–105, doi.
- A. Katsevich, 3PI algorithms for helical computer tomography, Advances in Applied Mathematics 36 (2006), pp.213–250, doi.
- M. Kapralov and A. Katsevich, A one-PI algorithm for helical trajectories that violate the convexity condition, Inverse Problems 22 (2006), pp.2123–2143, doi.
- A. Katsevich and M. Kapralov, Filtered backprojection inversion of the cone beam transform for a general class of curves, SIAM Journal Applied Mathematics, 68 (2007), pp.334–353, doi.
- M. Kapralov and A. Katsevich, A study of 1PI algorithms for a general class of curves, SIAM Journal Imaging Science, 1 (2008), pp.418–459, doi.
Limied Data
- D. Finch, I.-R. Lan and G. Uhlmann, Microlocal analysis of the x-ray transform with sources on a curve, Inside out: inverse problems and applications, Mathematical Sciences Research Institute Publications, 47 (2003), pp.193--218, pdf.
- J. Frikel and E. T. Quinto, Limited Data Problems for the Generalized Radon Transform in Rⁿ, SIAM Journal on Mathematical Analysis, 48 (2016), pp.2301--2318, doi.
- C. Mathison, Sampling in thermoacoustic tomography, Journal of Inverse and Ill-posed Problems, 28 (2020), pp.881–897, doi.
- E. T. Quinto, Singularities of the X-Ray Transform and Limited Data Tomography in R² and R³, SIAM Journal on Mathematical Analysis, 24 (1993), pp.1215--1225, doi.
Integral Geometry related with Rader, Sonar, Seismic Imaging and etc
- Clemens Kirisits, Michael Quellmalz, Monika Ritsch-Marte, Otmar Scherzer, Eric Setterqvist and Gabriele Steidl, Fourier reconstruction for diffraction tomography of an object rotated into arbitrary orientations, Inverse Problems, 37(11) (2021), 115002, doi.
- J. Christensen, F. Gonzalez and T. Kakehi, Surjectivity of mean value operators on noncompact symmetric spaces, Journal of Functional Analysis, 272(9) (2017), pp.3610–3646, doi.
- R. Felea, R. Gaburro, A. Greenleaf and C. Nolan, Microlocal analysis of borehole seismic data, Inverse Problems and Imaging, May 2022, doi.
- Y. Okada and H. Yamane, Generalized spherical mean value operators on Euclidean space, Tsukuba Journal of Mathematics, 45(1) (2021), pp.37-50, doi.
- E. T. Quinto, A. Rieder and T. Schuster, Local inversion of the sonar transform regularized by the approximate inverse, Inverse Problems 27(3) (2011), 035006, 18 pp, doi.
- B. Rubin, Higher-rank Radon transforms on constant curvature spaces, arXiv:2110.04832.
- B. Rubin, A note on the sonar transform and related Radon transforms, arXiv:2206.05854
Tensor Tomography
- S. Gouezel and T. Lefeuvre, Analysis & PDE 14 (2021), pp.301-322, Classical and microlocal analysis of the X-ray transform on Anosov manifolds, doi.
- C. Guillarmou, M. Lassas and L. Tzou, X-Ray Transform in Asymptotically Conic Spaces, International Mathematics Research Notices, Volume 2022, Issue 5 (2022), pp.3918-3976, doi.
- M. V. de Hoop, G. Uhlmann and J. Zhai, Inverting the local geodesic ray transform of higher rank tensors, Inverse Problems, 35 (2019), 115009, doi.
- J. Ilmavirta, On Radon transform on tori, Journal of Fourier Analysis and Applications, 21, (2015), pp.370-382, doi.
- J. Ilmavirta and G. Uhlmann, Tensor tomography in periodic slabs, Journal of Functional Analysis, 275, (2018), pp.288-299, doi.
- J. Ilmavirta and K. M\"onkk\"onen, Unique continuation of the normal operator of the X-ray transform and applications in geophysics, Inverse Problems, 36, (2020), 045014, doi.
- J. Ilmavirta and K. M\"onkk\"onen, X-ray tomography of one-forms with partial data, SIAM Journal on Mathematical Analysis, 53(3) (2021), doi.
- J. Ilmavirta and F. Monard, Integral geometry on manifolds with boundary and applications, "The Radon Transform: The First 100 Years and Beyond" (Ronny Ramlau, Otmar Scherzer, eds.), de Gruyter, 2019, doi.
- J. Rallo, Fourier analysis of periodic Radon transforms, Journal of Fourier Analysis and Applications, 26 (2020), Article Number 64, doi.
- V. P. Krishnan and R. K. Mishra, Microlocal Analysis of a Restricted Ray Transform on Symmetric m-Tensor Fields in Rⁿ, SIAM Journal on Mathematical Analysis, 50 (2016), pp.6230--6254, doi.
- G. P. Paternain, M. Salo and G. Uhlmann, Tensor tomography: Progress and challenges, Chinese Annales of Mathematics, Series B, 35 (2014), pp.399--428, doi.
- G. P. Paternain, M. Salo and G. Uhlmann, Invariant distributions, Beurling transforms and tensor tomography in higher dimensions, Mathematische Annalen, 363 (2015), pp.305--362, doi.
- P. Stefanov, G. Uhlmann, A. Vasy and H. Zhou, Travel time tomography, Acta Mathematica Sinica, English Series, 35 (2019), pp.1085--1114, doi.
- P. Stefanov, G. Uhlmann and A. Vasy, Local and global boundary rigidity and the geodesic X-ray transform in the normal gauge, Annales of Mathematics, 194 (2021), pp.1--95, doi.
X-ray transform
- L. Pestov and G. Uhlmann, On characterization of the range and inversion formulas for the geodesic X-ray transform, International Mathematics Research Notices, 2004, no. 80, pp.4331–4347, doi.
- Colin Guillarmou, Matti Lassas, and Leo Tzou, X-ray Transform in Asymptotically Conic Spaces, International Mathematics Research Notices, 2022(5) (2022), pp.3918-3976, doi.
- V. P. Krishnan, On the inversion formulas of Pestov and Uhlmann for the geodesic ray transform, Journal of Inverse and Ill-Posed Problems, 18 (2010), pp.401–408, doi.
- C. Guillarmou and F. Monard, Reconstruction formulas for X-ray transforms in negative curvature, Annles de L'Institut Fourier (Grenoble), 67 (2017), pp.1353–1392, doi.
- S. Holman and G. Uhlmann, On the microlocal analysis of the geodesic X-ray transform with conjugate points, Journal of Differential Geometry, 108 (2018), pp.459-494, doi.
Original Papers or Surveys on Basic Ideas
- A. M. Cormack, Representation of a function by its line integrals, with some radiological applications, Journal of Applied physics, 34 (1963), pp.2722, doi.
- A. M. Cormack, Representation of a function by its line integrals, with some radiological applications. II, Journal of Applied physics, 35 (1964), pp.2908, doi.
- P. C. Lauterbur, Image formation by induced local interactions: Examples employing nuclear magnetic resonance, Nature, 242 (1973), pp.190--191, doi.
- G. Beylkin, The inversion problem and applications of the generalized radon transform, Communications on Pure and Applied Mathematics, 37 (1984), pp.579--599, doi.
- M. Cheney, D. Isaacson and J. C. Newell, Electrical impedance tomography, SIAM Review, 41 (1999), pp.85--101, doi.
- L. Borcea, Electrical impedance tomography, Inverse Problems, 18 (2002), pp.R99--R136, doi.
Some Papers on Integral Geometry and Geometric Tomography
- C. L. Epstein, Introduction to magnetic resonance imaging for mathematicians, Annales de l'institut Fourier (Grenoble), 54 (2004), pp.1697--1716, doi.
- T. Kaasalainen, M. Ekholm, T. Siiskonen, and M. Kortesniemi, Dental cone beam CT: An updated review, European Journal of Medical Physics, 88 (2021), pp.193--217, doi.
- P. Kuchment and L. Kunyansky, Mathematics of thermoacoustic tomography, European Journal of Applied Mathematics, 19 (2008), pp.191--224, doi.
- P. Kuchment and L. Kunyansky, Mathematics of Photoacoustic and Thermoacoustic Tomography, Handbook of mathematical methods in imaging. Vol. 1, 2, 3, pp.817--867, Springer, New York, 2015, doi.
- R. G. Novikov, An inversion formula for the attenuated X-ray transformation, Arkiv för Matematik, 40 (2002), pp.145--167, doi.
- V. P. Palamodov, A uniform reconstruction formula in integral geometry, Inverse Problems, 28 (2012), 065014, doi.
- B. Rubin, Overdetermined Transforms in Integral Geometry, Complex Analysis and Dynamical Systems VI: Part 1: PDE, Differential Geometry, Radon Transform, pp.291--313, Contemporary Mathematics, 653, American Mathematical Society, 2015, doi.
- E. C. Tarabusi and M. A. Picardello, Radon transform in hyperbolic spaces and their discrete counterparts, Complex Analysis and Operator Theory, 15 (2021), article number 13, doi.
- Ｇ. Varoquaux and V. Cheplygina Machine learning for medical imaging: methodological failures and recommendations for the future, npj Digital Medicine, 5(48) (2022), doi.
Some Books on Integral Geometry and Geometric Tomography
- C. L. Epstein, "Introduction to the mathematics of medical imaging, Second edition", SIAM, 2008, doi.
- R. J. Gardner, "Geometric Tomograhy" Second Edition, Encyclopedia of Mathematics and its Applications, 58, Cambridge University Press, 1995, 2006, url.
- S. Helgason, "Integral Geometry and Radon Transforms", Springer, 2011, doi.
- P. Kuchment, "The Radon Transform and Medical Imaging", CBMS-NSF Regional Conference Series in Applied Mathematics, SIAM, 2014, doi.
- F. Natterer, "The Mathematics of Computerized Tomography", SIAM, 2001, doi.
- F. Natterer and F. Wübbeling, "Mathematical Methods in Image Reconstruction", SIAM monographs on mathematical modeling and computation, SIAM, 2001, doi.
- G. Olafsson and E. T. Quinto eds, "The Radon Transform, Inverse Problems, and Tomography", Proceedings of Symposia in Applied Mathematics, 63, American Mathematical Society, Providence, RI, 2006, url.
- V. Palamodov, "Reconstructive Integral Geometry", Springer, 2004, url.
- V. Palamodov, "Reconstruction from Integral Data", Chapman and Hall/CRC, 2016, url.
- R. Ramlau and O. Scherzer eds, "The Radon Transform - The First 100 Years and Beyond", Radon Series on Computational and Applied Mathematics 22, de Gruyter, 2019, url.
- B. Rubin, "Introduction to Radon Transforms, With Elements of Fractional Calculus and Harmonic Analysis", Encyclopedia of Mathematics and its Applications, 160, Cambridge University Press, 2015, url.
- O. Scherzer eds, "Handbook of Mathematical methods in Imaging", Springer, 2015, url, contents.
- V. A. Sharafutdinov, "Integral Geometry of Tensor Fields", Inverse and Ill-Posed Problems Series, 1, De Gruyter, 1994, doi.
Inverse Problems
- Ru-Yu Lai, Gunther Uhlmann, and Yang Yang, "Reconstruction of the collision kernel in the nonlinear Boltzmann equation", SIAM Journal on Mathematical Analysis, 53(1) (2021), pp.1049-1069, doi.

Mathematics of Data Science / Computational Science

Computer Program
- K. Kim, 3D Cone beam CT (CBCT) projection backprojection FDK, iterative reconstruction Matlab examples, MATLAB Central File Exchange, Retrieved March 8, 2021, url.
Calculus, Linear Algebra, Scientific Programming and etc
- Alan Edelman, David P. Sanders and Charles E. Leiserson, MIT 18.S191 Introduction to Computational Thinking, Spring 2021, url
- G. Strang, "Linear Algebra and Learning from Data", Wellesley Publishers, 2018, support page.
- G. Strang, MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018, YouTube Playlist.
- C. Eckart and G. Young, The approximation of one matrix by another of lower rank, Psychometrika, 1 (1936), pp.211–218, doi.
- M. Udell and A. Townsend, Why are big data matrices approximately low rank?, SIAM Journal on Mathematics of Data Science, 1, (2019), pp.144-160, doi.
- T. Y. Hou, Z. Li and Z. Zhang, Analysis of asymptotic escape of strict saddle sets in manifold optimization, SIAM Journal on Mathematics of Data Science, 2, (2020), pp.840-871, doi.
Sampling and Visualization
- H. Andrade-Loarca, G. Kutyniok, O. Öktem, and P. C. Petersen, Extraction of digital wavefront sets using applied harmonic analysis and deep neural network, SIAM Journal on Imaging Sciences, 12 (2019), pp.1936--1966, doi.
- T. A. Bubba, G. Kutyniok, M. Lassas, M. Marz, W. Samek, S. Siltanen and V. Srinivasan, Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography, Inverse Problems, 35 (2019), 064002, doi.
- C. L. Epstein, How well does the finite Fourier transform approximate the Fourier transform? , Communications on Pure and Applied Mathematics, 58 (2005), pp.1421--1435, doi.
- H. Andrade-Loarca, G. Kutyniok and O. Öktem Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm, Proceedings of the Royal Society A: Mathematical, Phisical and Engineering Science, 25 November 2020, doi.
- G. Kutyniok and D. Labate, Resolution of the wavefront set using continuous shearlets, Transactions of the American Mathematical Society, 361 (2009), pp.2719--2754, doi.
- P. Grohs, Continuous shearlet frames and resolution of the wavefront set, Monatshefte für Mathematik, 164 (2011), pp.393--426, doi.
- B. Han, S. Paul and N. K. Shukla, Microlocal analysis and characterization of Sobolev wavefront sets using shearlets, Constructive Approximation, (2021), doi.
- P. Stefanov, Semiclassical sampling and discretization of certain linear inverse problems, SIAM Journal on Mathematical Analysis, 52 (2020), pp.5554–5597, doi.
- P. Stefanov and S. Tindel, Sampling linear inverse problems with noise, Asymptotic Analysis, in press, doi.
- P. Stefanov, The Radon transform with finitely many angles, arXiv:2208.05936 .
- F. Monard and P. Stefanov, Sampling the X-ray transform on simple surfaces, arXiv:2110.05761.
- L. Bétermin, M. Faulhuber and S. Steinerberger A variational principle for Gaussian lattice sums, arXiv:2110.06008, 1W-MINDS, 10 March 2022.
Mathematics of Machine Learning
- I. Daubechies, R. DeVore, S. Foucart, B. Hanin and G. Petrova, Nonlinear approximation and (deep) ReLU networks, Constructive Approximation, 55, (2022), pp.127-172, doi.
- Ingrid Daubechies, Ronald DeVore, Nadav Dym, Shira Faigenbaum-Golovin, Shahar Z. Kovalsky, Kung-Chin Lin, Josiah Park, Guergana Petrova, and Barak Sober, Neural network approximation of refinable functions, IEEE Transactions on Information Theory, ?, (2022), pp.?-?, in press, doi.
- C. Fefferman, S. Ivanov, Y. Kulylev, M. Lassas and H. Narayanan, Reconstruction of a Riemannian manifolds I: The geometric Whitney problem, Foundations of Computational Mathematics, 20, (2020), pp.1035-1133, doi.
- C. Fefferman, S. Ivanov, M. Lassas J. Lu and H. Narayanan, Reconstruction of a Riemannian manifolds II: Inverse problems for Riemannian manifolds with partial distance data, arXiv:2111.14528.
- C. Fefferman, S. Ivanov, M. Lassas and H. Narayanan, Reconstruction of a Riemannian manifold from noisy intrinsic distances, SIAM Journal on Mathematics of Data Science, 2, (2020), pp.770-808, doi.
- Jianfeng Lu, Zuowei Shen, Haizhao Yang, and Shijun Zhang, Deep network approximation for smooth functions, SIAM Journal on Mathematical Analysis, 53(5), (2021), pp.5465-5506, doi.
- Zuowei Shen, Haizhao Yang, and Shijun Zhang, Optimal approximation rate of ReLU networks in terms of width and depth, Journal de Mathématiques Pures et Appliquées, 157, (2022), pp.101-135, doi.
- Qianxiao Li, Ting Lin, and Zuowei Shen, Deep learning via dynamical dystems: an approximation perspective, Journal of European Mathematical Society, in press.
Using Machine Learning
- H. Andrade-Loarca, G. Kutyniok, O. Öktem and P. Petersen, Deep microlocal reconstruction for limited-angle tomography, Applied and Computational Harmonic Analysis, 59 (2022), pp.155-197, doi.
- T. A. Bubba, M. Galinier, M. Lassas, M. Prato, L. Ratti and S. Siltanen, Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography, SIAM Journal of Imaging Science, 14 (2021), pp.470-505, doi.
- J. Feliu-Fabà, Y. Fan and L. Ying, Meta-learning pseudo-differential operators with deep neural networks, Journal of Computational Physics, 408, (2020), 109309, 18 pages, doi.
- Thomas Hofmann, Bernhard Schölkopf and Alexander J. Smola, Kernel methods in machine learning, Annals of Statistics, 36 (2008), pp.1171-1220, doi.
- Chang Min Hyun, Seong Hyeon Baek, Mingyu Lee, Sung Min Lee and Jin Keun Seo, Deep Learning-Based Solvability of Underdetermined Inverse Problems in Medical Imaging, Medical Image Analysis, 69 (2021), 101967, doi.
- Hyoung Suk Park, Jin Keun Seo, Chang Min Hyun, Sung Min Lee, and Kiwan Jeon, A fidelity-embedded learning for metal artifact reduction in dental CBCT, Medical Physics, 18 May 2022, doi.
- Gunther Uhlmann and Yiran Wang, Convolutional neural networks in phase space and inverse problems, SIAM Journal on Applied Mathematics, 80(6) (2020), pp.2560–2585, doi.
Geometric Data Analysis
- Parvaneh Joharinad and Jürgen Jost, Geometry of data, arXiv:2203.07208.
- Raul Rabadan and Andrew J. Blumberg, "Topological Data Analysis for Genomics and Evolution", Cambridge University Press, 2019, doi.
Non-Line-of-Sight Imaging
- Matthew O'Toole, David B. Lindell and Gordon Wetzstein, Confocal non-line-of-sight imaging based on the light-cone transform, Nature, 555 (2018), pp.338-341, doi.
- Shumian Xin, Sotiris Nousias, Kiriakos N. Kutulakos, Aswin C. Sankaranarayanan, Srinivasa G. Narasimhan, and Ioannis Gkioulekas, A theory of Fermat paths for non-line-of-sight shape reconstruction, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp.6793-6802, doi.
- Chia-Yin Tsai, Aswin C. Sankaranarayanan, and Ioannis Gkioulekas, Beyond volumetric albedo — A surface optimization framework for non-line-of-sight imaging, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp.1545-1555, doi.
- Daniele Faccio, Andreas Velten, and Gordon Wetzstein Non-line-of-sight imaging, Nature Reviews Physics, 2 (2020), pp.318-327, doi.
- Sean I. Young, David B. Lindell, Bernd Girod, David Taubman, and Gordon Wetzstein, Non-line-of-sight surface reconstruction using the directional light-cone transform, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp.1404-1413, doi.
- Xintong Liu, Jianyu Wang, Zhupeng Li, Zuoqiang Shi, Xing Fu, and Lingyun Qiu, Non-line-of-sight reconstruction with signal–object collaborative regularization, Light: Science & Applications, 10 (2021), article number 198, doi.
- Chengquan Pei, Anke Zhang, Yue Deng, Feihu Xu, Jiamin Wu, David U-Lei Li, Hui Qiao, Lu Fang, and Qionghai Dai, Dynamic non-line-of-sight imaging system based on the optimization of point spread functions, Optics Express, 29(20) (2021), pp.32349-32364, doi.
- Bin Wang, Ming-Yang Zheng, Jin-Jian Han, Xin Huang, Xiu-Ping Xie, Feihu Xu, Qiang Zhang, and Jian-Wei Pan, Non-line-of-sight imaging with picosecond temporal resolution, Physical Review Letters, 127(5) (2021), pp.053602, doi.
- Cheng Wu, Jianjiang Liu, Xin Huang, Zheng-Ping Li, Chao Yu, Jun-Tian Ye, Jun Zhang, Qiang Zhang, Xiankang Dou, Vivek K. Goyal, and Feihu Xu, Non-line-of-sight imaging over 1.43 km, The Proceedings of National Academy of Science, 118(10) (2021), article number e2024468118, doi.
- Zhupeng Li, Xintong Liu, Jianyu Wang, Zuoqiang Shi, Lingyun Qiu, and Xing Fu, Fast non-line-of-sight imaging based on first photon event stamping, Optics Letters, 47(8) (2022), pp.1928-1931, doi.
- Pablo Luesia, Miguel Crespo, Adrian Jarabo, and Albert Redo-Sanchez, Non-line-of-sight imaging in the presence of scattering media using phasor fields, Optics Letters, 47(15) (2022), pp.3796-3799, doi.
- Wenqing Yang, Chao Zhang, Wenjie Jiang, Zexin Zhang, and Baoqing Sun, None-line-of-sight imaging enhanced with spatial multiplexing, Optics Express, 30(4) (2022), pp.5855-5867, doi.
Computational Science
- L. A. Peletier and J. Gabrielsson, Impact of mathematical pharmacology on practice and theory: four case studies, Journal of Pharmacokinetics and Pharmacodynamics, 45 (2018), pp.3-21, doi.

Geometric and Microlocal Analysis except for Tomography

Boutet de Monvel's Calculus and Index Theory
- E. Schrohe, A short introduction to Boutet de Monvel's calculus, Operator Theory: Advances and Applications, 125, pp.85-116, Springer, 2001, doi, pdf.
- V. Nazaikinskii, B.-W. Schulze and B. Sternin, "The Localization Problem in Index Theory of Elliptic Operators", Pseudo=Differential Operators, 10, Birkhäuser, 2014, doi.

Semiclassical Analysis and Complex Geometry
- H. Hezari and Z. Lu and H. Xu, Off-diagonal Asymptotic Properties of Bergman Kernels Associated to Analytic Kähler Potentials, International Mathematics Research Notices, Volume 2020, Issue 8 (2020), pp. 2241–2286, doi.
- O. Rouby, J. Sjöstrand and S. V. Ngoc, Analytic Bergman operators in the semiclassical limit, Duke Math. J., 169(16) (2020), pp.3033-3097, doi.
- H. Hezari and H. Xu, On a property of Bergman kernels when the Kähler potential is analytic, Pacific Journal of Mathematics, 313(2) (2021), pp.413-432, doi.
- A. Deleporte, M. Hitrik and J. Sjöstrand, A direct approach to the analytic Bergman projection, arXiv:2004.14606.
- X. Ma and G. Marinescu, Berezin Toeplitz quantization on Kähler manifolds, Journal für die reine und angewandte Mathematik, 662 (2012), pp.1--56, doi.

Analysis on Generalized Heisenberg Groups
- A. Kable, On certain conformally invariant systems of differential equations, New York Journal of Mathematics, 19 (2013), pp.189--251, url.
- A. Kable, On certain conformally invariant systems of differential equations II, Tsukuba Journal of Mathematics, 39 (2015), pp.39--81, doi.

Analysis on the Poincaré Disk
- S. Zelditch, Pseudodifferential analysis on hyperbolic surfaces, J. Funct. Anal., 68 (1986), pp.72-105, doi.
- W. Bauer and L. A. Coburn, Heat flow, weighted Bergman spaces, and real analytic Lipschitz approximation, J. Reine Angew. Math., 703 (2015), pp.225-246, doi.

Some Topics in Applied Mathematics
- Applications of Fourier Analysis on the Euclidean Space
  - Paolo Boggiatto, Carmen Fernández, Antonio Galbis, Alessandro Oliaro Wigner transform and quasicrystals, Journal of Functional Analysis, 282(6) (2022), article number 109374, doi.
  - T. Strohmer, Pseudodifferential operators and Banach algebras in mobile communications, Applied and Computational Harmonic Analysis, 20 (2006), pp.237--249, doi.
    In this paper, mobile communication is formulated in terms of pseudodifferential operators of order zero, and some finite dimensional approximation is proposed.
  - A. Koldobsky, "Fourier Analysis in Convex Geometry", SURV 116, American Mathematical Society, 2005, url.
- Shannon's Celebrating Papers:
  - C. E. Shannon, A mathematical theory of communication, the Bell System Technical Journal, 27(3) (1948), pp.379-423, doi, pdf.
  - C. E. Shannon, A mathematical theory of communication, the Bell System Technical Journal, 27(4) (1948), pp.623-656, doi.
  - C. E. Shannon, Communication in the presence of noise, Proceedings of the Institute of Radio Engineers, 37(1) (1949), pp.10-21, doi, pdf.
- Compressive Sampling
  - E. J. Candes, J. K. Romberg and T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, 52 (2006), pp.489--509, doi, pdf.
  - E. J. Candes, J. K. Romberg and T. Tao, Stable signal recovery from incomplete and inaccurate measurements, Communications on Pure and Applied Mathematics, 59 (2006), pp.1207--1233, doi, pdf.
  - E. J. Candes and T. Tao, Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?, IEEE Transactions on Information Theory, 52 (2006), pp.5406--5425, doi, pdf.