Zobrazeno 1 - 10
of 736
pro vyhledávání: '"Sijbers, Jan"'
Autor:
Nguyen, Anh-Tuan, Renders, Jens, Iuso, Domenico, Maris, Yves, Soete, Jeroen, Wevers, Martine, Sijbers, Jan, De Beenhouwer, Jan
In four-dimensional computed tomography (4DCT), 3D images of moving or deforming samples are reconstructed from a set of 2D projection images. Recent techniques for iterative motion-compensated reconstruction either necessitate a reference acquisitio
Externí odkaz:
http://arxiv.org/abs/2402.04480
Autor:
Iuso, Domenico, Chatterjee, Soumick, Cornelissen, Sven, Verhees, Dries, De Beenhouwer, Jan, Sijbers, Jan
Additive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure that quality standards are met in all manufactured samples of a batch, X-ray computed tomography (X-CT) is
Externí odkaz:
http://arxiv.org/abs/2305.07894
Current state-of-the-art motion-based dynamic computed tomography reconstruction techniques estimate the deformation by considering motion models in the entire object volume although occasionally the proper change is local. In this article, we addres
Externí odkaz:
http://arxiv.org/abs/2301.11029
Autor:
Renders, Jens, Shafieizargar, Banafshe, Verhoye, Marleen, De Beenhouwer, Jan, Dekker, Arnold J. den, Sijbers, Jan
Longitudinal MRI is an important diagnostic imaging tool for evaluating the effects of treatment and monitoring disease progression. However, MRI, and particularly longitudinal MRI, is known to be time consuming. To accelerate imaging, compressed sen
Externí odkaz:
http://arxiv.org/abs/2301.09455
The design of new x-ray phase contrast imaging setups often relies on Monte Carlo simulations for prospective parameter studies. Monte Carlo simulations are known to be accurate but time consuming, leading to long simulation times, especially when ma
Externí odkaz:
http://arxiv.org/abs/2208.04137
Publikováno v:
In Precision Engineering October 2024 90:108-121
Autor:
Beirinckx, Quinten, Bladt, Piet, van der Plas, Merlijn C.E., van Osch, Matthias J.P., Jeurissen, Ben, den Dekker, Arnold J., Sijbers, Jan
Publikováno v:
In NeuroImage 1 February 2024 286
Restoring high-quality CT images from low dose CT counterparts is an ill-posed, nonlinear problem to which Deep Learning approaches have been giving superior solutions compared to classical model-based approaches. In this article, a framework is pres
Externí odkaz:
http://arxiv.org/abs/1910.06565
Low Dose Computed Tomography suffers from a high amount of noise and/or undersampling artefacts in the reconstructed image. In the current article, a Deep Learning technique is exploited as a regularization term for the iterative reconstruction metho
Externí odkaz:
http://arxiv.org/abs/1906.00650
This is an article about the Computed Tomography (CT) and how Deep Learning influences CT reconstruction pipeline, especially in low dose scenarios.
Externí odkaz:
http://arxiv.org/abs/1904.03908