Zobrazeno 1 - 10
of 455
pro vyhledávání: '"Schneider, Linda"'
Autor:
Ye, Chengze, Schneider, Linda-Sophie, Sun, Yipeng, Thies, Mareike, Mei, Siyuan, Maier, Andreas
This paper introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits using a differentiable shift-variant filtered backprojection (FBP) neural network. Traditional CBCT reconstruction methods for ar
Externí odkaz:
http://arxiv.org/abs/2410.14900
Autor:
Schiering, Nadine, Eichstaedt, Sascha, Heizmann, Michael, Koch, Wolfgang, Schneider, Linda-Sophie, Scheele, Stephan, Sommer, Klaus-Dieter
Mathematical models of measuring systems and processes play an essential role in metrology and practical measurements. They form the basis for understanding and evaluating measurements, their results and their trustworthiness. Classic analytical para
Externí odkaz:
http://arxiv.org/abs/2408.06117
Autor:
Schneider, Linda-Sophie, Krauss, Patrick, Schiering, Nadine, Syben, Christopher, Schielein, Richard, Maier, Andreas
Mathematical models are vital to the field of metrology, playing a key role in the derivation of measurement results and the calculation of uncertainties from measurement data, informed by an understanding of the measurement process. These models gen
Externí odkaz:
http://arxiv.org/abs/2406.16659
Recent advances in computed tomography (CT) imaging, especially with dual-robot systems, have introduced new challenges for scan trajectory optimization. This paper presents a novel approach using Gated Recurrent Units (GRUs) to optimize CT scan traj
Externí odkaz:
http://arxiv.org/abs/2405.09333
Autor:
Sun, Yipeng, Huang, Yixing, Schneider, Linda-Sophie, Thies, Mareike, Gu, Mingxuan, Mei, Siyuan, Bayer, Siming, Maier, Andreas
Computed Tomography (CT) image reconstruction is crucial for accurate diagnosis and deep learning approaches have demonstrated significant potential in improving reconstruction quality. However, the choice of loss function profoundly affects the reco
Externí odkaz:
http://arxiv.org/abs/2403.10695
This study presents a novel approach for reconstructing cone beam computed tomography (CBCT) for specific orbits using known operator learning. Unlike traditional methods, this technique employs a filtered backprojection type (FBP-type) algorithm, wh
Externí odkaz:
http://arxiv.org/abs/2403.00426
X-ray computed tomography (CT) plays a key role in digitizing three-dimensional structures for a wide range of medical and industrial applications. Traditional CT systems often rely on standard circular and helical scan trajectories, which may not be
Externí odkaz:
http://arxiv.org/abs/2402.10223
The rise of deep learning has introduced a transformative era in the field of image processing, particularly in the context of computed tomography. Deep learning has made a significant contribution to the field of industrial Computed Tomography. Howe
Externí odkaz:
http://arxiv.org/abs/2401.16104
Autor:
Sun, Yipeng, Schneider, Linda-Sophie, Fan, Fuxin, Thies, Mareike, Gu, Mingxuan, Mei, Siyuan, Zhou, Yuzhong, Bayer, Siming, Maier, Andreas
In this study, we introduce a Fourier series-based trainable filter for computed tomography (CT) reconstruction within the filtered backprojection (FBP) framework. This method overcomes the limitation in noise reduction by optimizing Fourier series c
Externí odkaz:
http://arxiv.org/abs/2401.16039
Autor:
Thies, Mareike, Wagner, Fabian, Maul, Noah, Yu, Haijun, Goldmann, Manuela, Schneider, Linda-Sophie, Gu, Mingxuan, Mei, Siyuan, Folle, Lukas, Preuhs, Alexander, Manhart, Michael, Maier, Andreas
Publikováno v:
in IEEE Transactions on Medical Imaging (2024)
Cone-beam computed tomography (CBCT) systems, with their flexibility, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such as acute stroke assessment. However, the integration of CBCT into clini
Externí odkaz:
http://arxiv.org/abs/2401.09283