41 citations · 63 across the 17 of their papers we have counts for
17 papers
Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT: From Simulated to Real Data
Nikita Moriakov, Efstratios Gavves, Jonathan H. Mason +3
Cone Beam CT (CBCT) is an important imaging modality nowadays, however lower image quality of CBCT compared to more conventional Computed Tomography (CT) remains a limiting factor…
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke +1
Cardiac MRI (CMRI) is a cornerstone imaging modality that provides in-depth insights into cardiac structure and function. Multi-contrast CMRI (MCCMRI), which acquires sequences wit…
Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT
Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
Cone Beam CT (CBCT) is an essential imaging modality nowadays, but the image quality of CBCT still lags behind the high quality standards established by the conventional Computed T…
Joint Supervised and Self-supervised Learning for MRI Reconstruction
George Yiasemis, Nikita Moriakov, Clara I. Sánchez +2
Magnetic Resonance Imaging (MRI) represents an important diagnostic modality; however, its inherently slow acquisition process poses challenges in obtaining fully-sampled -space…
Deep Cardiac MRI Reconstruction with ADMM
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke +1
Cardiac magnetic resonance imaging is a valuable non-invasive tool for identifying cardiovascular diseases. For instance, Cine MRI is the benchmark modality for assessing the cardi…
vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke +1
Medical Imaging (MI) tasks, such as accelerated parallel Magnetic Resonance Imaging (MRI), often involve reconstructing an image from noisy or incomplete measurements. This amounts…
Improving Lesion Volume Measurements on Digital Mammograms
Nikita Moriakov, Jim Peters, Ritse Mann +4
Lesion volume is an important predictor for prognosis in breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model…
Neural Modulation Fields for Conditional Cone Beam Neural Tomography
Samuele Papa, David M. Knigge, Riccardo Valperga +4
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex…
End-to-end Memory-Efficient Reconstruction for Cone Beam CT
Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
Cone Beam CT plays an important role in many medical fields nowadays, but the potential of this imaging modality is hampered by lower image quality compared to the conventional CT.…
Subpixel object segmentation using wavelets and multi resolution analysis
Ray Sheombarsing, Nikita Moriakov, Jan-Jakob Sonke +1
We propose a novel deep learning framework for fast prediction of boundaries of two-dimensional simply connected domains using wavelets and Multi Resolution Analysis (MRA). The bou…
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations
Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos +20
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific communi…
Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis
Koen Michielsen, Nikita Moriakov, Jonas Teuwen +1
Reconstruction of digital breast tomosynthesis is a challenging problem due to the limited angle data available in such systems. Due to memory limitations, deep learning-based meth…
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation
Jonas Teuwen, Nikita Moriakov, Christian Fedon +7
The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital…
Kernel of CycleGAN as a Principle homogeneous space
Nikita Moriakov, Jonas Adler, Jonas Teuwen
Unpaired image-to-image translation has attracted significant interest due to the invention of CycleGAN, a method which utilizes a combination of adversarial and cycle consistency…
Learned SIRT for Cone Beam Computed Tomography Reconstruction
Roeland J. Dilz, Lukas Schröder, Nikita Moriakov +2
We introduce the learned simultaneous iterative reconstruction technique (SIRT) for tomographic reconstruction. The learned SIRT algorithm is a deep learning based reconstruction m…
Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation
Joris van Vugt, Elena Marchiori, Ritse Mann +3
Computer-aided detection aims to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. DM exams are generated by devices from…
Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction
Nikita Moriakov, Koen Michielsen, Jonas Adler +3
Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular r…