Literatur zum Projekt und Referenzen der Projektleiter

[1] J. Abhau, Z. Belhachmi, and O. Scherzer. On a decomposition model for optical flow. In Energy Minimization Methods in Computer Vision and Pattern Recognition, pages 126–139. Springer, 2009.
[2] G. Adluru, E. V. DiBella, and M. Schabel. Model-based registration for dynamic cardiac perfusion MRI. J Magn Reson Imaging, 24(5):1062–1070, 2006.
[3] M. Benning. A nonlinear variational method for improved quantification of myocardial blood flow using dynamic H215O PET. Diploma thesis, University of Muenster, WWU, june 2008.
[4] M. Benning, T. Kösters, F. Wübbeling, K. Schäfers, and M. Burger. A nonlinear variational method for improved quantification of myocardial blood flow using dynamic h 2 15 o pet. In Nuclear Science Symposium Conference Record, 2008. NSS’08. IEEE, pages 4472–4477. IEEE, 2008.
[5] M. Bhushan, J. A. Schnabel, L. Risser, et al. Motion correction and parameter estimation in dceMRI sequences: application to colorectal cancer. In Proc. MICCAI 2011, pages 476–483. Springer, Toronto, 2011.
[6] M. Birke, N. Bissantz, and H. Holzmann. Confidence bands for inverse regression models. Inverse Problems, 26:115020, 2010.
[7] K. Bissantz, N. Bissantz, and K. Proksch. Monitoring of significant changes over time in fluorescence microscopy imaging of living cells - technical details. Technical report sfb 823, tu dortmund, 2015.
[8] N. Bissantz, L. Dümbgen, H. Holzmann, and A. Munk. Nonparametric confidence bands in deconvolution density estimation. Journal of the Royal Statistical Society, Ser. B., 69:483–506, 2007.
[9] N. Bissantz, T. Hohage, A. Munk, and F. Ruymgaart. Convergence rates of general regularization methods for statistical inverse problems and applications. Siam J. Numer. Anal., 45:2610–2636, 2007.
[10] N. Bissantz, H. Holzmann, and K. Proksch. Confidence regions for images observed under the Radon transform. Journal of Multivariate Analysis, 128:86–107, 2013.
[11] N. Bissantz, B. Mair, and A. Munk. A multi-scale stopping criterion for MLEM reconstructions in PET. IEEE Nucl. Sci. Symp. Conf. Rec., 6:3376–3379, 2006.
[12] N. Bissantz, B. Mair, and A. Munk. A statistical stopping rule for mlem reconstructions in pet. IEEE Nucl. Sci. Symp. Conf. Rec., 8:41984200, 2008.
[13] L. Bokacheva, H. Rusinek, J. L. Zhang, et al. Estimates of glomerular filtration rate from MR renography and tracer kinetic models. J Magn Reson Imaging, 29(2):371–382, 2009.
[14] C. Brune. 4d imaging in tomography and optical nanoscopy. PhD thesis, PhD thesis, University of Münster, Germany, 2010.
[15] D. Buckley, A. Shurrab, C. Cheung, et al. Measurement of single kidney function using dynamic contrast-enhanced MRI: Comparison of two models in human subjects. J Magn Reson Imaging, 24(5):1117–1123, 2006.
[16] S. Bundesamt. Todesursachen, 2014. [Online; accessed 04.04.2016].
[17] G. Buonaccorsi, J. O’Connor, A. Caunce, et al. Tracer kinetic model–driven registration for dynamic contrast-enhanced MRI time-series data. Magn Reson Med, 58(5):1010–1019, 2007.
[18] M. Burger, J. Müller, E. Papoutsellis, and C.-B. Schönlieb. Total variation regularization in measurement and image space for pet reconstruction. Inverse Problems, 30(10):105003, 2014.
[19] F. Büther, M. Dawood, L. Stegger, F. Wübbeling, M. Schäfers, O. Schober, and K. P. Schäfers. List mode–driven cardiac and respiratory gating in PET. Journal of Nuclear Medicine, 50(5):674–681, 2009.
[20] M. Cutajar, I. Mendichovszky, P. Tofts, and I. Gordon. The importance of AIF ROI selection in DCE-MRI renography: reproducibility and variability of renal perfusion and filtration. Eur J Radiol, 74(3):e154–e160, 2010.
[21] M. Dawood, T. Kösters, M. Fieseler, F. Büther, X. Jiang, F. Wübbeling, and K. P. Schäfers. Motion correction in respiratory gated cardiac pet/ct using multi-scale optical flow. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2008, pages 155–162. Springer,
[22] H. Dette and B. Hetzler. A simple test for the parametric form of the variance function in nonparametric regression. Annals of the Institute of Statistical Mathematics, 61:861–886, 2009.
[23] H. Dette, N. Neumeyer, and I. Van Keilegom. A new test for the parametric form of the variance function in nonparametric regression. Journal of the Royal Statistical Society, Ser. B, 69:903–917, 2007.
[24] H. Dirks. Variational Methods for Joint Motion Estimation and Image Reconstruction. PhD thesis, 2015.
[25] H. Dirks. A flexible primal-dual toolbox. arXiv preprint arXiv:1603.05835, 2016.
[26] E. Eikefjord, E. Andersen, E. Hodneland, et al. Use of 3d DCE-MRI for the estimation of renal perfusion and glomerular filtration rate: An intrasubject comparison of FLASH and KWIC with a comprehensive framework for evaluation. Am J Roentgenol, 204(3):W273–W281, 2015.
[27] R. Engbers, M. Benning, P. Heins, K. Schafers, and M. Burger. Sparse recovery in myocardial blood flow quantification via pet. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE, pages 3742–3744. IEEE, 2011.
[28] M. Fieseler, T. Kösters, F. Gigengack, H. Braun, H. H. Quick, K. P. Schäfers, and X. Jiang. Motion correction in pet-mri: a human torso phantom study. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE, pages 3586–3588. IEEE, 2011.
[29] F. Gigengack, X. Jiang, M. Dawood, and K. P. Schäfers. Motion estimation. In Motion Correction in Thoracic Positron Emission Tomography, pages 21–63. Springer, 2015.
[30] F. Gigengack, L. Ruthotto, X. Jiang, J. Modersitzki, M. Burger, S. Hermann, and K. P. Schäfers. Atlas-based whole-body pet-ct segmentation using a passive contour distance. In Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging, pages 82–92. Springer, 2012.
[31] N. Hackstein, J. Heckrodt, and W. Rau. Measurement of single-kidney glomerular filtration rate using a contrast-enhanced dynamic gradient-echo sequence and the Rutland-Patlak plot technique. J Magn Reson Imaging, 18(6):714–725, 2003.
[32] V. Hamy, N. Dikaios, S. Punwani, , et al. Respiratory motion correction in dynamic mri using robust data decomposition registration–application to DCE-MRI. Med Image Anal, 18(2):301–313, 2014.
[33] C. Heck and E. Hanson. A model-driven registration framework for DCE-MRI. Abstract for ISMRM Workshop on Motion Correction in MRI (Tromso), 2014.
[34] P. Heins. Reconstruction Using Local Sparsity: A Novel Regularization Technique and an Asymptotic Analysis of Spatial Sparsity Priors. PhD thesis, 2014.
[35] E. Hodneland, E. Hanson, A. Lundervold, J. Modersitzki, et al. Segmentation-driven image registration-application to 4D DCE-MRI recordings of the moving kidneys. IEEE Trans Image Process, 23(5):2392–2404, 2014.
[36] J. Modersitzki. FAIR – Flexible Algorithms for Image Registration., 2008.
[37] S. Keeling and K. Kunisch. Robust L1 approaches to computing the geometric median and principal and independent components, 2014.
[38] T. Kösters, K. P. Schäfers, and F. Wübbeling. Emrecon: An expectation maximization based image reconstruction framework for emission tomography data. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE, pages 4365–4368. IEEE, 2011.
[39] V. Lee, H. Rusinek, L. Bokacheva, et al. Renal function measurements from MR renography and a simplified multicompartmental model. Am J Physiol Renal Physiol, 292(5):F1548–F1559, 2007.
[40] J. Machac. Cardiac positron emission tomography imaging. Semin. Nucl. Med., 35:17–36, 2005.
[41] A. Melbourne, D. Atkinson, M. White, et al. Registration of dynamic contrast-enhanced MRI using a progressive principal component registration (PPCR). Phys Med Biol, 52(17):5147, 2007.
[42] J. Modersitzki. Numerical Methods for Image Registration. Oxford University Press, New York, 2004.
[43] J. Modersitzki. FLIRT with rigidity – image registration with a local non-rigidity penalty. IJCV, pages 153–163, 2008. DOI: 10.1007/s11263-007-0079-3.
[44] J. Modersitzki. FAIR: Flexible Algorithms for Image Registration. SIAM, Philadelphia, 2009.
[45] F. Natterer and F. Wuebbeling. Mathematical methods in image reconstruction. Siam, 2001.
[46] N. Neumeyer and H. Dette. Testing for symmetric error distribution in nonparametric regression models. Statistica Sinica, 17:775–795, 2007.
[47] N. Neumeyer, H. Dette, and E.-R. Nagel. Bootstrap tests for the error distribution in linear and nonparametric regression models. Australian & New Zealand Journal of Statistics, 48(2):129–156, 2006.
[48] C. Patlak, R. Blasberg, and J. Fenstermacher. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab, 3(1):1–7, 1983.
[49] K. Proksch, N. Bissantz, and H. Dette. Confidence bands for multivariate and time dependent inverse regression models. Bernoulli, 21:144–175, 2015.
[50] A. J. Reader, F. C. Sureau, C. Comtat, R. Tr´ ebossen, and I. Buvat. Joint estimation of dynamic pet images and temporal basis functions using fully 4d ml em. Physics in Medicine and Biology, 51(21):5455, 2006.
[51] L. Reips. Parameter Identification in Medical Imaging. PhD thesis, PhD thesis, University of Münster, Germany, 2013.
[52] L. Reips, M. Burger, and R. Engbers. Towards dynamic pet reconstruction under flow conditions: Parameter identification in a pde model. arXiv preprint arXiv:1411.5143, 2014. Submitted.
[53] L. Reips, R. Engbers, and M. Burger. Dynamic pet reconstruction based on a reaction-diffusion model. PAMM, 12(1):683–684, 2012.
[54] L. Ruthotto, F. Gigengack, M. Burger, C. H. Wolters, X. Jiang, K. P. Schäfers, and J. Modersitzki. A simplified pipeline for motion correction in dual gated cardiac pet. In Bildverarbeitung für die Medizin 2012, pages 51–56. Springer, 2012.
[55] L. Ruthotto and J. Modersitzki. Non-linear image registration. In O. Scherzer, editor, Handbook of Mathematical Methods of Imaging. Springer, New York, 2014. Submitted.
[56] M. Rutland. A single injection technique for subtraction of blood background in 131I-hippuran renograms. Br J Radiol, 52(614):134–137, 1979.
[57] S. S. Technical aspects of MR perfusion. Eur J Radiol., 76(3):304–13, 2010.
[58] A. Sawatzky, C. Brune, F. Wübbeling, T. Kosters, K. Schäfers, and M. Burger. Accurate em-tv algorithm in pet with low snr. In Nuclear Science Symposium Conference Record, 2008. NSS’08. IEEE, pages 5133–5137. IEEE, 2008.
[59] M. Schellmann, S. Gorlatch, D. Meiländer, T. Kösters, K. Schäfers, F. Wübbeling, and M. Burger. Parallel medical image reconstruction: from graphics processors to grids. In Parallel Computing Technologies, pages 457–473. Springer, 2009.
[60] S. P. Sourbron, H. J. Michaely, M. F. Reiser, et al. MRI-measurement of perfusion and glomerular filtration in the human kidney with a separable compartment model. Invest Radiol, 43(1):40–48, 2008.
[61] C. Thomas and L. Thomas. Renal failure — measuring the glomerular filtration rate. Dtsch Arztebl Int, 106(51-52):849–54, 2009.
[62] P. Tofts, M. Cutajar, I. Mendichovszky, A. Peters, and I. Gordon. Precise measurement of renal filtration and vascular parameters using a two-compartment model for dynamic contrast-enhanced MRI of the kidney gives realistic normal values. Eur Radiol., 22(6):1320–30, 2012.
[63] M. Travin. Cardiac neuronal imaging at the edge of clinical application. Cardiol. Clin., 27:311–327, 2009.
[64] R. Vogl, D. Rudolph, H. Angenent, A. Thoring, C. Schild, S. Stieglitz, and C. Meske. sciebo–the campuscloud: a sync and share cloud storage service for the academic and research community in north rhine-westphalia. PIK-Praxis der Informationsverarbeitung und Kommunikation, 38(3-4):105–112, 2016.
[65] Wikipedia. Glomeruläre Filtrationsrate — Wikipedia, the free encyclopedia, 2016. [Online;accessed 31.02.2016].
[66] Y. Zhang, C. Wu, J. Zhang, et al. Feasibility study of high-resolution DCE-MRI for glomerular filtration rate (GFR) measurement in a routine clinical modal. Magn Reson Imaging, 33(8):978–983, 2015.
[67] F. Zöollner, A. Lundervold, M. Kocinski, and J. Roorvik. Model-based parameter estimation in DCE-MRI without an arterial input function. In Proc. BVM 2007 (Aachen), pages 237–241. Springer Vieweg, 2007.