[1] J. S. Jørgensen and E. Y. Sidky. How little data is enough? Phase-diagram analysis of sparsity-regularized X-ray computed tomography. Philosophical Transactions of Royal Society of London. Series A. Mathematical, Physical, and Engineering Sciences, 373:20140387, 2015. [ bib ]
[2] J. S. Jørgensen, C. Kruschel, and D. A. Lorenz. Testable uniqueness conditions for empirical assessment of undersampling levels in total variation-regularized X-ray CT. Inverse Problems in Science and Engineering, Published online, 2014. [ bib | DOI | http ]
[3] J. S. Jørgensen, E. Y. Sidky, P. C. Hansen, and X. Pan. Empirical average-case relation between undersampling and sparsity in X-ray CT. Inverse Problems and Imaging, 9(2):431-446, 2015. [ bib | DOI ]
[4] J. S. Jørgensen, E. Y. Sidky, and X. Pan. Quantifying admissible undersampling for sparsity-exploiting iterative image reconstruction in X-ray CT. IEEE Transactions on Medical Imaging, 32:460-473, 2013. [ bib | DOI ]
[5] P. A. Wolf, J. S. Jørgensen, T. G. Schmidt, and E. Y Sidky. Few-view single photon emission computed tomography (SPECT) reconstruction based on a blurred piecewise constant object model. Physics in Medicine and Biology, 58:5629-5652, 2013. [ bib ]
[6] J. S. Jørgensen, E. Y. Sidky, and X. Pan. Connecting image sparsity and sampling in iterative reconstruction for limited angle X-ray CT. In Proceedings of the 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, pages 169-172, Lake Tahoe, CA, United States, 2013. [ bib ]
[7] E. Y. Sidky, R. Chartrand, J. S. Jørgensen, and X. Pan. Nonconvex optimization for improved exploitation of gradient sparsity in ct image reconstruction. In Proceedings of the 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, pages 189-192, Lake Tahoe, CA, United States, 2013. [ bib ]
[8] E. Y. Sidky, J. H. Jørgensen, and X. Pan. Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm. Physics in Medicine and Biology, 57:3065-3091, 2012. [ bib | DOI ]
[9] E. Y. Sidky, J. S. Jørgensen, and X. Pan. First-order convex feasibility algorithms for x-ray CT. Medical Physics, 40:031115, 2013. [ bib | DOI ]
[10] T. L. Jensen, J. H. Jørgensen, P. C. Hansen, and S. H. Jensen. Implementation of an optimal first-order method for strongly convex total variation regularization. BIT Numerical Mathematics, 52:329-356, 2012. [ bib | DOI ]
[11] J. H. Jørgensen and A. Skajaa. The Matlab Syntax. Polyteknisk Forlag, 2012. Introductory reference for using MATLAB (translation of Danish book Matlab Syntaksen), 169 pages. [ bib | http ]
[12] J. H. Jørgensen and A. Skajaa. Matlab Syntaksen. Polyteknisk Forlag, 2012. Introductory reference for using MATLAB, 169 pages. [ bib | http ]
[13] J. H. Jørgensen, E. Y. Sidky, and X. Pan. Toward quantifying admissible undersampling of sparsity-exploiting iterative image reconstruction for X-ray CT. In Proceedings of The Second International Conference on Image Formation in X-Ray Computed Tomography, pages 161-164, Salt Lake City, UT, United States, 2012. [ bib ]
[14] E. Y. Sidky, J. H. Jørgensen, and X. Pan. Convex optimization prototyping for iterative image reconstruction in X-ray CT. In Proceedings of The Second International Conference on Image Formation in X-Ray Computed Tomography, pages 343-347, Salt Lake City, UT, United States, 2012. [ bib ]
[15] E. Y. Sidky, J. H. Jørgensen, and X. Pan. Sampling conditions for gradient-magnitude sparsity based image reconstruction algorithms. In N. J. Pelc, R. J. Nishikawa, and B. R. Whiting, editors, Medical Imaging 2012: Physics of Medical Imaging, Proceedings of SPIE, volume 8313, page 831337, San Diego, CA, United States, 2012. [ bib ]
[16] E. Y. Sidky, J. H. Jørgensen, and X. Pan. Characterizing a discrete-to-discrete X-ray transform for iterative image reconstruction with limited angular-range scanning in CT. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2012 IEEE, pages 3387-3389, Anaheim, CA, United States, 2012. [ bib | DOI ]
[17] E. Y. Sidky, J. H. Jørgensen, and X. Pan. Convergence of iterative image reconstruction algorithms for digital breast tomosynthesis. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2012 IEEE, pages 3394-3396, Anaheim, CA, United States, 2012. [ bib | DOI ]
[18] P. Wolf, J. H. Jørgensen, T. Gilat-Schmidt, and E. Y. Sidky. A first-order primal-dual reconstruction algorithm for few-view SPECT. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2012 IEEE, pages 2381-2385, Anaheim, CA, United States, 2012. [ bib | DOI ]
[19] J. H. Jørgensen, E. Y. Sidky, and X. Pan. Ensuring convergence in total-variation-based reconstruction for accurate microcalcification imaging in breast X-ray CT. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE, pages 2640-2643, Valencia, Spain, 2011. [ bib ]
[20] P. C. Hansen and J. H. Jørgensen. Total variation and tomographic imaging from projections. In Proceedings of WSC 2011, Conference of the Dutch-Flemish Numerical Analysis Communities, Woudschouten (Invited conference contribution), pages 44-51, Zeist, The Netherlands, 2011. [ bib ]
[21] J. H. Jørgensen, T. L. Jensen, P. C. Hansen, S. H. Jensen, E. Y. Sidky, and X. Pan. Accelerated gradient methods for total-variation-based CT image reconstruction. In Proceedings of the 11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, pages 435-438, Potsdam, Germany, 2011. Available from http://arxiv.org/abs/1105.4002. [ bib | http ]
[22] J. H. Jørgensen, P. C. Hansen, E. Y. Sidky, I. S. Reiser, and X. Pan. Toward optimal X-ray flux utilization in breast CT. In Proceedings of the 11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, pages 359-362, Potsdam, Germany, 2011. Available from http://arxiv.org/abs/1104.1588. [ bib | http ]
[23] J. H. Jørgensen, E. Y. Sidky, and X. Pan. Reliable small-object reconstruction from sparse views in X-ray computed tomography. In Proceedings of the 4th Workshop on Signal Processing with Adaptive Sparse Structured Representations, Edinburgh, Scotland, United Kingdom, 2011. [ bib ]
[24] A. Skajaa and J. H. Jørgensen. Find Formlen - Matematik. Polyteknisk Forlag, 2nd edition, 2010. Collection of Formulas, 140 pages (1st Edition, 2006, 110 pages). [ bib | http ]
[25] T. L. Jensen, J. H. Jørgensen, P. C. Hansen, and S. H. Jensen. Public-domain software: TVReg, November 2010. Available from: http://www.imm.dtu.dk/~pch/TVReg/. Matlab routines for total variation-regularized tomographic image reconstruction. [ bib | http ]
[26] J. H. Jørgensen. Public-domain software: tomobox, August 2010. Available from http://www.mathworks.com/matlabcentral/fileexchange/28496-tomobox. Matlab routines for numerical simulation of CT imaging. [ bib | http ]
[27] J. H. Jørgensen. Knowledge-based tomography algorithms. Master's thesis, Technical University of Denmark, 2009. [ bib | .pdf ]
[28] J. H. Jørgensen. The use of deconvolution in the analysis of impedance spectroscopy data. Internal report BC-959, Fuel Cells and Solid State Chemistry Division, Risø DTU National Laboratory for Sustainable Energy, 2008. [ bib ]

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