Publications
Nikita Moriakov
2024
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Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CTN. Moriakov, J. Sonke, J. Teuwen, 2024
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Deep Cardiac MRI Reconstruction with ADMMStatistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge PapersG. Yiasemis, N. Moriakov, J. Sonke, J. Teuwen, 2024
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Deep learning-based low-dose CT simulator for non-linear reconstruction methods.Medical physicsS. A. M. Tunissen, N. Moriakov, M. Mikerov, E. J. Smit, I. Sechopoulos, J. Teuwen, 2024
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Improving lesion volume measurements on digital mammogramsMedical Image AnalysisN. Moriakov, J. Peters, R. Mann, N. Karssemeijer, J. Van Dijck, M. Broeders, J. Teuwen, 2024
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Deep learning‐based low‐dose CT simulator for non‐linear reconstruction methodsMedical PhysicsS. A. M. Tunissen, N. Moriakov, M. Mikerov, E. J. Smit, I. Sechopoulos, J. Teuwen, 2024, 51;(9):6046-6060
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Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement NetworkG. Yiasemis, N. Moriakov, J. Sonke, J. Teuwen, 2024
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2023
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Development, validation, and simplification of a scanner‐specific CT simulatorMedical PhysicsS. A. M. Tunissen, L. J. Oostveen, N. Moriakov, J. Teuwen, K. Michielsen, E. J. Smit, I. Sechopoulos, 2023
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vSHARP: variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse-ProblemsG. Yiasemis, N. Moriakov, J. Sonke, J. Teuwen, 2023
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End‐to‐end memory‐efficient reconstruction for cone beam CTMedical PhysicsN. Moriakov, J. Sonke, J. Teuwen, 2023, 12;(50):7579-7593
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JSSL: Joint Supervised and Self-supervised Learning for MRI ReconstructionG. Yiasemis, N. Moriakov, C. I. Sánchez, J. Sonke, J. Teuwen, 2023
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Joint machine learning and analytic track reconstruction for X-ray polarimetry with gas pixel detectorsAstronomy & AstrophysicsN. 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
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2022
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Multi-Coil MRI Reconstruction Challenge—Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil ConfigurationsFrontiers in NeuroscienceY. 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
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DIRECT: Deep Image REConstruction ToolkitJournal of Open Source SoftwareG. Yiasemis, N. Moriakov, D. Karkalousos, M. Caan, J. Teuwen, 2022, 7;(73):4278
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Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRIDiagnosticsR. Samperna, N. Moriakov, N. Karssemeijer, J. Teuwen, R. M. Mann, 2022, 12;(7):1690
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Prediction of histological grade and molecular subtypes of invasive breast cancer using mammographic growth rate in screeningEuropean Journal of CancerJ. Peters, N. Moriakov, J. Van Dijck, S. Elias, E. Lips, J. Wesseling, R. Mann, J. Teuwen, M. Caballo, M. Broeders, 2022, 175
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2021
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Subpixel object segmentation using wavelets and multi resolution analysisR. Sheombarsing, N. Moriakov, J. Sonke, J. Teuwen, 2021
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Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimationMedical Image AnalysisJ. Teuwen, N. Moriakov, C. Fedon, M. Caballo, I. Reiser, P. Bakic, E. García, O. Diaz, K. Michielsen, I. Sechopoulos, 2021, 71
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Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI ChallengeMachine Learning for Medical Image ReconstructionP. 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
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2020
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Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis6th International Conference on Image Formation in X-Ray Computed Tomography (CT-meeting)K. Michielsen, N. Moriakov, J. Teuwen, I. Sechopoulos, 2020
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Convolutional neural networksHandbook of Medical Image Computing and Computer Assisted InterventionJ. Teuwen, N. Moriakov, 2020
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Computable Følner monotilings and a theorem of BrudnoErgodic Theory and Dynamical SystemsN. Moriakov, 2020, 41;(11):3389-3416
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Kernel of cyclegan as a principal homogeneous spaceInternational Conference on Learning RepresentationsN. Moriakov, J. Adler, J. Teuwen, 2020
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2019
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Learned SIRT for Cone Beam Computed Tomography ReconstructionR. J. Dilz, L. Schröder, N. Moriakov, J. Sonke, J. Teuwen, 2019
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Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptationMedical Imaging 2019: Computer-Aided DiagnosisJ. Teuwen, N. Moriakov, R. Mann, E. Marchiori, J. Van Vugt, A. Gubern-Mérida, 2019
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Deep learning framework for digital breast tomosynthesis reconstructionMedical Imaging 2019: Physics of Medical ImagingN. Moriakov, K. Michielsen, J. Adler, R. Mann, I. Sechopoulos, J. Teuwen, 2019
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i-RIM applied to the fastMRI challengeP. Putzky, D. Karkalousos, J. Teuwen, N. Moriakov, B. Bakker, M. Caan, M. Welling, 2019
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2018
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Fluctuations of ergodic averages for actions of groups of polynomial growthStudia MathematicaN. Moriakov, 2018, 240;(3):255-273
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On systems with quasi-discrete spectrumStudia MathematicaM. Haase, N. Moriakov, 2018, 241;(2):173-199
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2017
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On Effective Birkhoff’s Ergodic Theorem for Computable Actions of Amenable GroupsTheory of Computing SystemsN. Moriakov, 2017, 62;(5):1269-1287
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