Prof. Dr. Jan Modersitzki

Publications

2020

Polzin T, Niethammer M, Vialard F-X, Modersitzki J (2020) A discretize-optimize approach for LDDMM registration. In: Pennec X, Sommer S, Fletcher T (eds) Riemannian Geometric Statistics in Medical Image Analysis. Academic Press, pp 479–532

2018

Brehmer K, Wacker B, Modersitzki J (2018) A Novel Similarity Measure for Image Sequences. In: Klein S, Staring M, Durrleman S, Sommer S (eds) Biomedical Image Registration. WBIR 2018. LNCS 10883, pp 47–56

2017

Rühaak J, König L, Tramnitzke F, Köstler H, Modersitzki J (2017) A Matrix-Free Approach to Efficient Affine-Linear Image Registration on CPU and GPU. J Real-Time Image Proc 13(1):205–225
Rühaak J, Polzin T, Heldmann S, Simpson IJA, Handels H, Modersitzki J, Heinrich MP (2017) Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration. IEEE Trans Med Imaging 36(8):1746–1757
Rust C, Haeger S, Traulsen N, Modersitzki J (2017) A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images. Current Directions in Biomedical Engineering. pp 533–537
Ruthotto L, Greif C, Modersitzki J (2017) A stabilized multigrid solver for hyperelastic image registration: Multigrid Preconditioner for Hyperelastic Image Registration. Numer Linear Algebra Appl 24(5):e2095

2016

Lotz J, Olesch J, Müller B, Polzin T, Galuschka P, Lotz JM, Heldmann S, Laue H, González-Vallinas M, Warth A, Lahrmann B, Grabe N, Sedlaczek O, Breuhahn K, Modersitzki J (2016) Patch-Based Nonlinear Image Registration for Gigapixel Whole Slide Images. IEEE Transactions on Biomedical Engineering 63(9):1812–1819
Lotz J, Olesch J, Müller B, Polzin T, Galuschka P, Lotz JM, Heldmann S, Laue H, Warth A, Lahrmann B, Grabe N, Sedlaczek O, Breuhahn K, Modersitzki J (2016) Patch-Based Nonlinear Image Registration for Gigapixel Whole Slide Images. IEEE Trans Biomed Eng 63(9):1812–1819
Polzin T, Niethammer M, Heinrich MP, Handels H, Modersitzki J (2016) Memory Efficient LDDMM for Lung CT. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Lecture Notes in Computer Science 9902(Part III), pp 28–36
Sandmann C, Hodneland E, Modersitzki J (2016) A practical guideline for T1 reconstruction from various flip angles in MRI. J Algorithms Comput Technol 10(4):213–223

2015

Hallmann M, Modersitzki J, Dammann P, Miller D, Sure U (2015) Ultrasound – MRI Fusion for Image – Guided Neurosurgery. Proc. of CURAC 2015. pp 207–211
Horn M, Nolde J, Goltz J, Barkhausen J, Schade W, Waltermann C, Modersitzki J, Olesch J, Papenberg N, Keck T, Kleemann M (2015) Ein Prototyp für die navigierte Implantation von Aortenstentprothesen zur Reduzierung der Kontrastmittel- und Strahlenbelastung: Das Nav-CARS-EVAR-Konzept (Navigated-Contrast-Agent and Radiation Sparing Endovascular Aortic Repair). Zentralbl Chir 140(05):493–499
König L, Derksen A, Heldmann S, Papenberg N, Modersitzki J, Haas B (2015) OC-0409: Deformable image registration with guaranteed local rigidity. Radiotherapy and Oncology: Proceedings of the 3rd ESTRO Forum. pp S197–S198
Modersitzki J, Heldmann S, Papenberg N (2015) Nonlinear Registration Via Displacement Fields. In: Toga AW (ed) Brain Mapping. Academic Press, Waltham, pp 307–314
Papenberg N, König L, Heldmann S, Derksen A, Modersitzki J, Haas B (2015) A Locally Variable Transformation Model for Deformable Image Registration Using Tissue Properties. Varian Research Partnership Symposium. Atlanta, Georgia, USA
Weiss N, Lotz J, Modersitzki J (2015) Multimodal Image Registration in Digital Pathology Using Cell Nuclei Densities. Bildverarbeitung für die Medizin 2015. pp 245–250

2014

Polzin T, Rühaak J, Werner R, Handels H, Modersitzki J (2014) Lung Registration using Automatically Detected Landmarks. Methods Inf Med 53(4):250–256
Tramnitzke F, Rühaak J, König L, Modersitzki J, Köstler H (2014) GPU Based Affine Linear Image Registration using Normalized Gradient Fields. Proc. Seventh International Workshop on High Performance Computing for Biomedical Image Analysis (HPC-MICCAI). Boston, USA, pp 5–14

2013

Barendt S, Modersitzki J (2013) A Variational Model for SPECT Reconstruction with a Nonlinearly Transformed Attenuation Prototype. Int J Comput Math 90(1):82–91
Burger M, Modersitzki J, Ruthotto L (2013) A Hyperelastic Regularization Energy for Image Registration. SIAM J Sci Comput 35(1):B132–B148
König L, Papenberg N, Haas B, Modersitzki J (2013) Deformable Registration for Adaptive Radiotherapy with Guaranteed Local Rigidity Constraints. Varian Research Partnership Symposium. Atlanta, Georgia, USA
Polzin T, Rühaak J, Werner R, Strehlow J, Heldmann S, Handels H, Modersitzki J (2013) Combining Automatic Landmark Detection and Variational Methods for Lung CT Registration. Proc. Fifth International MICCAI Workshop on Pulmonary Image Analysis (PIA 2013)
Ruthotto L, Mohammadi S, Heck C, Modersitzki J, Weiskopf N (2013) HySco – Hyperelastic Susceptibility Artifact Correction of DTI in SPM. Bildverarbeitung für die Medizin. Springer-Verlag Berlin/Heidelberg, pp 51–56

2012

Ruthotto L, Gigengack F, Burger M, Wolters C, Jiang X, Schäfers K, Modersitzki J (2012) A Simplified Pipeline for Motion Correction in Dual Gated Cardiac PET. Bildverarbeitung für die Medizin. pp 51–56
Ruthotto L, Hodneland E, Modersitzki J (2012) Registration of Dynamic Contrast Enhanced MRI with Local Rigidity Constraint. Proceedings of WBIR. LNCS 7359, pp 190–198
Ruthotto L, Kugel H, Olesch J, Fischer B, Modersitzki J, Burger M, Wolters C (2012) Diffeomorphic Susceptibility Artifact Correction of Diffusion-Weighted Magnetic Resonance Images. Phys Med Biol 57(18):5715–5731

2011

Barendt S, Modersitzki J (2011) SPECT Reconstruction with a Nonlinear Transformed Attenuation Prototype. Bildverarbeitung für die Medizin. pp 414–418
Zimmer V, Papenberg N, Modersitzki J, Fischer B (2011) Bildregistrierung zur Verbrennungsanalyse. Bildverarbeitung für die Medizin. pp 159–163

2010

Haber E, Horesh R, Modersitzki J (2010) Numerical Methods for Constrained Image Registration. Numerical Linear Algebra with Applications 17:343–359
Pöschel C, Modersitzki J, Scherzer O (2010) A Variational Setting for Volume Constrained Image Registration. Inverse Problems and Imaging. pp 502–522

2009

Barendt S, Fischer B, Modersitzki J (2009) A Kernel Representation for Exponential Splines with Global Tension. Proceedings of SPIE Medical Imaging: Image Processing. 72450I:pp 1–10
Ens K, Heldmann S, Modersitzki J, Fischer B (2009) Improving an affine and non-linear image registration and/or segmentation task by incorporating characteristics of the displacement field. Proceedings of SPIE Medical Imaging. 725932:pp 1–8
Haber E, Heldmann S, Modersitzki J (2009) A Computational Framework for Image-Based Constrained Registration. Linear Algebra and Its Applications 431(3-4):459–470
Haber E, Heldmann S, Modersitzki J (2009) A Scale-Space Approach to Landmark Constrained Image Registration. Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision. pp 612–623
Lange T, Papenberg N, Heldmann S, Modersitzki J, Fischer B, Lamecker H, Schlag PM (2009) 3D ultrasound-CT registration of the liver using combined landmark-intensity information. Int J CARS 4(1):79–88
Schumacher H, Modersitzki J, Fischer B (2009) Combined Reconstruction and Motion Correction in SPECT Imaging. IEEE Trans Nucl Sci 56(1):73–80
Schumacher H, Modersitzki J, Fischer B (2009) Reconstruction and motion correction in SPECT imaging – a combined approach. Proceedings of the 12th Korea-Germany joint workshop on Advanced Medical Image Processing. pp 32–41

2008

Haber E, Heldmann S, Modersitzki J (2008) Adaptive Mesh Refinement for Nonparametric Image Registration. SIAM J Sci Comput 30(6):3012–3027
Papenberg N, Modersitzki J, Fischer B (2008) Registrierung im Fokus. Bildverarbeitung für die Medizin. Informatik aktuell 7, pp 138–142

2007

Papenberg N, Schumacher H, Heldmann S, Wirtz S, Bommersheim S, Ens K, Modersitzki J, Fischer B (2007) A Fast and Flexible Image Registration Toolbox – Design and Implementation of the general approach. Bildverarbeitung für die Medizin. SPIE Medical Imaging, pp 106–110