no code implementations • 20 Jan 2024 • Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
Our method surpasses classical and deep learning baselines, including LIRE, on the thorax test set.
no code implementations • 27 Nov 2023 • George Yiasemis, Nikita Moriakov, Clara I. Sánchez, Jan-Jakob Sonke, Jonas Teuwen
In this paper, we introduce JSSL (Joint Supervised and Self-supervised Learning), a novel training approach for deep learning-based MRI reconstruction algorithms aimed at enhancing reconstruction quality in scenarios where target dataset(s) containing fully sampled k-space measurements are unavailable.
no code implementations • 10 Oct 2023 • George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
In this work, inspired by related work in accelerated MRI reconstruction, we present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of dynamic cardiac imaging.
no code implementations • 18 Sep 2023 • George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
In this study, we propose vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems), a novel DL-based method for solving ill-posed inverse problems arising in MI.
no code implementations • 28 Aug 2023 • Nikita Moriakov, Jim Peters, Ritse Mann, Nico Karssemeijer, Jos van Dijck, Mireille Broeders, Jonas Teuwen
Finally, for a subset of 100 mammograms with a malign mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0. 81 [95% CI 0. 73 - 0. 87] for consistency and 0. 78 [95% CI 0. 66 - 0. 86] for absolute agreement.
no code implementations • 17 Jul 2023 • Samuele Papa, David M. Knigge, Riccardo Valperga, Nikita Moriakov, Miltos Kofinas, Jan-Jakob Sonke, Efstratios Gavves
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex class of Cone Beam Geometry CT (CBCT) reconstruction.
no code implementations • 28 Oct 2021 • Ray Sheombarsing, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
The effectiveness of the proposed method is demonstrated by delineating boundaries of simply connected domains (organs) in medical images using Debauches wavelets and comparing performance with a U-Net baseline.
no code implementations • 29 Sep 2021 • Ray Sheombarsing, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
The effectiveness of the proposed method is demonstrated by delineating boundaries of simply connected domains (organs) in medical images using Debauches wavelets and comparing performance with a U-Net baseline.
1 code implementation • 10 Nov 2020 • Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos, Nikita Moriakov, Mattha Caan, George Yiasemis, Lívia Rodrigues, Alexandre Lopes, Hélio Pedrini, Letícia Rittner, Maik Dannecker, Viktor Studenyak, Fabian Gröger, Devendra Vyas, Shahrooz Faghih-Roohi, Amrit Kumar Jethi, Jaya Chandra Raju, Mohanasankar Sivaprakasam, Mike Lasby, Nikita Nogovitsyn, Wallace Loos, Richard Frayne, Roberto Souza
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process.
no code implementations • 11 Jun 2020 • Jonas Teuwen, Nikita Moriakov, Christian Fedon, Marco Caballo, Ingrid Reiser, Pedrag Bakic, Eloy García, Oliver Diaz, Koen Michielsen, Ioannis Sechopoulos
This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks.
no code implementations • 16 May 2020 • Nikita Moriakov, Ashwin Samudre, Michela Negro, Fabian Gieseke, Sydney Otten, Luc Hendriks
We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXPE), a future NASA selected space-based mission expected to be operative in 2021.
no code implementations • ICLR 2020 • Nikita Moriakov, Jonas Adler, Jonas Teuwen
It is known that the CycleGAN problem might admit multiple solutions, and our goal in this paper is to analyze the space of exact solutions and to give perturbation bounds for approximate solutions.
no code implementations • 24 Jan 2020 • Nikita Moriakov, Jonas Adler, Jonas Teuwen
It is known that the CycleGAN problem might admit multiple solutions, and our goal in this paper is to analyze the space of exact solutions and to give perturbation bounds for approximate solutions.
no code implementations • 14 Aug 2018 • Nikita Moriakov, Koen Michielsen, Jonas Adler, Ritse Mann, Ioannis Sechopoulos, Jonas Teuwen
In this study we propose an extension of the Learned Primal-Dual algorithm for digital breast tomosynthesis.
no code implementations • 14 Aug 2018 • Joris van Vugt, Elena Marchiori, Ritse Mann, Albert Gubern-Mérida, Nikita Moriakov, Jonas Teuwen
We analyze two transfer learning settings: 1) unsupervised transfer, where Hologic data with soft lesion annotation at pixel level and Siemens unlabelled data are used to annotate images in the latter data; 2) weak supervised transfer, where exam level labels for images from the Siemens mammograph are available.