Publications

Nikita Moriakov

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
    Abstract
  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
    Abstract

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
    Abstract
  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
    Abstract

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
    Abstract
  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
    Abstract

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
    Abstract
  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
    Abstract
  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
    Abstract
  2. On systems with quasi-discrete spectrum
    Studia Mathematica
    M. Haase, N. Moriakov, 2018, 241;(2):173-199
    doi
    Abstract

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
    Abstract