Zobrazeno 1 - 10
of 289
pro vyhledávání: '"Knoll, Florian"'
Autor:
Vornehm, Marc, Chen, Chong, Sultan, Muhammad Ahmad, Arshad, Syed Murtaza, Han, Yuchi, Knoll, Florian, Ahmad, Rizwan
Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. Traditional breath-held imaging protocols, however, pose challenges for patients with arrhythmias or limited breath-holding capacity
Externí odkaz:
http://arxiv.org/abs/2412.04639
Transformer-based networks applied to image patches have achieved cutting-edge performance in many vision tasks. However, lacking the built-in bias of convolutional neural networks (CNN) for local image statistics, they require large datasets and mod
Externí odkaz:
http://arxiv.org/abs/2407.01367
Autor:
Solomon, Eddy, Johnson, Patricia M., Tan, Zhengguo, Tibrewala, Radhika, Lui, Yvonne W., Knoll, Florian, Moy, Linda, Kim, Sungheon Gene, Heacock, Laura
This data curation work introduces the first large-scale dataset of radial k-space and DICOM data for breast DCE-MRI acquired in diagnostic breast MRI exams. Our dataset includes case-level labels indicating patient age, menopause status, lesion stat
Externí odkaz:
http://arxiv.org/abs/2406.05270
The purpose of this study was to accelerate MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3T and 0.55T. A total of 35 healthy volunteers underwent conventional two-fold accelerated MRCP scans at fiel
Externí odkaz:
http://arxiv.org/abs/2405.03732
Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we addres
Externí odkaz:
http://arxiv.org/abs/2210.13834
Autor:
Hammernik, Kerstin, Küstner, Thomas, Yaman, Burhaneddin, Huang, Zhengnan, Rueckert, Daniel, Knoll, Florian, Akçakaya, Mehmet
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorp
Externí odkaz:
http://arxiv.org/abs/2203.12215
Purpose: To propose an alternating learning approach to learn the sampling pattern (SP) and the parameters of variational networks (VN) in accelerated parallel magnetic resonance imaging (MRI). Methods: The approach alternates between improving the S
Externí odkaz:
http://arxiv.org/abs/2110.14703
Autor:
Zhao, Ruiyang, Yaman, Burhaneddin, Zhang, Yuxin, Stewart, Russell, Dixon, Austin, Knoll, Florian, Huang, Zhengnan, Lui, Yvonne W., Hansen, Michael S., Lungren, Matthew P.
Improving speed and image quality of Magnetic Resonance Imaging (MRI) via novel reconstruction approaches remains one of the highest impact applications for deep learning in medical imaging. The fastMRI dataset, unique in that it contains large volum
Externí odkaz:
http://arxiv.org/abs/2109.03812