Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Guenter Lauritsch"'
Publikováno v:
IEEE Transactions on Medical Imaging. 40:3042-3053
Data truncation is a common problem in computed tomography (CT). Truncation causes cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the FOV. Deep learning has achieved impressive results in CT reconstruction
Autor:
Fatima Saad, Robert Frysch, Tim Pfeiffer, Sylvia Saalfeld, Jessica Schulz, Jens-Christoph Georgi, Andreas Nürnberger, Guenter Lauritsch, Georg Rose
Publikováno v:
7th International Conference on Image Formation in X-Ray Computed Tomography.
Publikováno v:
2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
Autor:
Andreas Maier, Guenter Lauritsch, Zijia Guo, Patrick Kugler, Mohammad Islam, Frédéric Noo, Florian Vogt
Publikováno v:
Physics in medicine and biology. 65(18)
Three-dimensional cone-beam imaging has become valuable in interventional radiology. Currently, this tool, referred to as C-arm CT, employs a circular short-scan for data acquisition, which limits the axial volume coverage and yields unavoidable cone
Publikováno v:
Medical Imaging 2020: Physics of Medical Imaging.
We present further progress on the implementation of C-arm CT imaging with the extended line-ellipse-line (LEL) trajectory. This novel data acquisition geometry is designed to enhance image quality in interventional radiology. Previously, we showed t
Publikováno v:
2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
Cone-beam (CB) imaging in interventional radiology, which we refer to as C-arm CT, is a valuable tool that could use further improvements to support growing clinical needs. Typically, a circular short-scan is used for data acquisition, which leads to
Autor:
Michael Manhart, Xiaolin Huang, Yixing Huang, Andreas Maier, Guenter Lauritsch, Alexander Preuhs
Publikováno v:
Machine Learning for Medical Image Reconstruction ISBN: 9783030338428
MLMIR@MICCAI
MLMIR@MICCAI
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: (a) due to insufficient training data the network may not generalize well to unseen data; (b) deep learning methods are sensitive to noise. Thus, gen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::424101ac8ebb6f95d1b88727cc4b50d5
https://doi.org/10.1007/978-3-030-33843-5_10
https://doi.org/10.1007/978-3-030-33843-5_10
Autor:
Nahid El Faquir, Guenter Lauritsch, Peter de Jaegere, Zouhair Rahhab, Nicolas M. Van Mieghem, Carl Schultz, Anne-Marie Maugenest, Ramón Rodríguez-Olivares
Publikováno v:
International Journal of Cardiovascular Imaging, 32(7), 1021-1029. Springer Netherlands
The International Journal of Cardiovascular Imaging
The International Journal of Cardiovascular Imaging
To study the determinants of image quality of rotational angiography using dedicated research prototype software for motion compensation without rapid ventricular pacing after the implantation of four commercially available catheter-based valves. Pro
Autor:
Ramón Rodríguez-Olivares, Nico Bruining, Nahid El Faquir, Carl Schultz, Marcel L. Geleijnse, Ben Ren, Guenter Lauritsch, Zouhair Rahhab, Nicolas M. Van Mieghem, Peter de Jaegere
Publikováno v:
Revista Española de Cardiología. 69:392-400
Resumen Introduccion y objetivos Se sabe que los factores relacionados con el paciente y con la intervencion se asocian con insuficiencia aortica despues de un implante percutaneo de valvula aortica. No obstante, tambien puede causarla una interaccio
Publikováno v:
International journal of computer assisted radiology and surgery. 14(1)
The application of traditional machine learning techniques, in the form of regression models based on conventional, "hand-crafted" features, to artifact reduction in limited angle tomography is investigated.Mean-variation-median (MVM), Laplacian, Hes