Publications

2024

1. Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT

   N. Moriakov, J. Sonke, J. Teuwen, 2024

   ArXiv abstract
2. Deep Cardiac MRI Reconstruction with ADMM

   Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers

   G. Yiasemis, N. Moriakov, J. Sonke, J. Teuwen, 2024

   doi preprints abstract

2023

1. Improving Lesion Volume Measurements on Digital Mammograms

   N. Moriakov, J. Peters, R. Mann, N. Karssemeijer, J. V. Dijck, M. Broeders, J. Teuwen, 2023

   ArXiv abstract
2. Development, validation, and simplification of a scanner‐specific CT simulator

   Medical Physics

   S. A. M. Tunissen, L. J. Oostveen, N. Moriakov, J. Teuwen, K. Michielsen, E. J. Smit, I. Sechopoulos, 2023

   doi abstract
3. vSHARP: variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse-Problems

   G. Yiasemis, N. Moriakov, J. Sonke, J. Teuwen, 2023

   ArXiv abstract
4. End‐to‐end memory‐efficient reconstruction for cone beam CT

   Medical Physics

   N. Moriakov, J. Sonke, J. Teuwen, 2023, 12;(50):7579-7593

   doi preprints abstract
5. JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction

   G. Yiasemis, N. Moriakov, C. I. Sánchez, J. Sonke, J. Teuwen, 2023

   ArXiv abstract
6. Joint machine learning and analytic track reconstruction for X-ray polarimetry with gas pixel detectors

   Astronomy & Astrophysics

   N. Cibrario, M. Negro, N. Moriakov, R. Bonino, L. Baldini, N. Di Lalla, L. Latronico, S. Maldera, A. Manfreda, N. Omodei, C. Sgró, S. Tugliani, 2023, 674

   doi abstract

2022

1. Multi-Coil MRI Reconstruction Challenge—Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations

   Frontiers in Neuroscience

   Y. Beauferris, J. Teuwen, D. Karkalousos, N. Moriakov, M. Caan, G. Yiasemis, L. Rodrigues, A. Lopes, H. Pedrini, L. Rittner, M. Dannecker, V. Studenyak, F. Gröger, D. Vyas, S. Faghih-Roohi, A. Kumar Jethi, J. Chandra Raju, M. Sivaprakasam, M. Lasby, N. Nogovitsyn, W. Loos, R. Frayne, R. Souza, 2022, 16

   doi preprints abstract
2. DIRECT: Deep Image REConstruction Toolkit

   Journal of Open Source Software

   G. Yiasemis, N. Moriakov, D. Karkalousos, M. Caan, J. Teuwen, 2022, 7;(73):4278

   doi
3. Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRI

   Diagnostics

   R. Samperna, N. Moriakov, N. Karssemeijer, J. Teuwen, R. M. Mann, 2022, 12;(7):1690

   doi abstract
4. Prediction of histological grade and molecular subtypes of invasive breast cancer using mammographic growth rate in screening

   European Journal of Cancer

   J. Peters, N. Moriakov, J. Van Dijck, S. Elias, E. Lips, J. Wesseling, R. Mann, J. Teuwen, M. Caballo, M. Broeders, 2022, 175

   doi

2021

1. Subpixel object segmentation using wavelets and multi resolution analysis

   R. Sheombarsing, N. Moriakov, J. Sonke, J. Teuwen, 2021

   ArXiv abstract
2. Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation

   Medical Image Analysis

   J. Teuwen, N. Moriakov, C. Fedon, M. Caballo, I. Reiser, P. Bakic, E. García, O. Diaz, K. Michielsen, I. Sechopoulos, 2021, 71

   doi preprints
3. Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge

   Machine Learning for Medical Image Reconstruction

   P. M. Johnson, G. Jeong, K. Hammernik, J. Schlemper, C. Qin, J. Duan, D. Rueckert, J. Lee, N. Pezzotti, E. De Weerdt, S. Yousefi, M. S. Elmahdy, J. H. F. Van Gemert, C. Schülke, M. Doneva, T. Nielsen, S. Kastryulin, B. P. F. Lelieveldt, M. J. P. Van Osch, M. Staring, E. Z. Chen, P. Wang, X. Chen, T. Chen, V. M. Patel, S. Sun, H. Shin, Y. Jun, T. Eo, S. Kim, T. Kim, D. Hwang, P. Putzky, D. Karkalousos, J. Teuwen, N. Moriakov, B. Bakker, M. Caan, M. Welling, M. J. Muckley, F. Knoll, 2021

   doi

2020

1. Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis

   6th International Conference on Image Formation in X-Ray Computed Tomography (CT-meeting)

   K. Michielsen, N. Moriakov, J. Teuwen, I. Sechopoulos, 2020

   ArXiv abstract
2. Convolutional neural networks

   Handbook of Medical Image Computing and Computer Assisted Intervention

   J. Teuwen, N. Moriakov, 2020

   doi
3. Computable Følner monotilings and a theorem of Brudno

   Ergodic Theory and Dynamical Systems

   N. Moriakov, 2020, 41;(11):3389-3416

   doi abstract
4. Kernel of cyclegan as a principal homogeneous space

   International Conference on Learning Representations

   N. Moriakov, J. Adler, J. Teuwen, 2020

   url preprints

2019

1. Learned SIRT for Cone Beam Computed Tomography Reconstruction

   R. J. Dilz, L. Schröder, N. Moriakov, J. Sonke, J. Teuwen, 2019

   ArXiv abstract
2. Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation

   Medical Imaging 2019: Computer-Aided Diagnosis

   J. Teuwen, N. Moriakov, R. Mann, E. Marchiori, J. Van Vugt, A. Gubern-Mérida, 2019

   doi preprints
3. Deep learning framework for digital breast tomosynthesis reconstruction

   Medical Imaging 2019: Physics of Medical Imaging

   N. Moriakov, K. Michielsen, J. Adler, R. Mann, I. Sechopoulos, J. Teuwen, 2019

   doi preprints
4. i-RIM applied to the fastMRI challenge

   P. Putzky, D. Karkalousos, J. Teuwen, N. Moriakov, B. Bakker, M. Caan, M. Welling, 2019

   ArXiv abstract

2018

1. Fluctuations of ergodic averages for actions of groups of polynomial growth

   Studia Mathematica

   N. Moriakov, 2018, 240;(3):255-273

   doi
2. On systems with quasi-discrete spectrum

   Studia Mathematica

   M. Haase, N. Moriakov, 2018, 241;(2):173-199

   doi

2017

1. On Effective Birkhoff’s Ergodic Theorem for Computable Actions of Amenable Groups

   Theory of Computing Systems

   N. Moriakov, 2017, 62;(5):1269-1287

   doi