Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Simon Arberet"'
Autor:
Luke A. Ginocchio, Paul N. Smereka, Angela Tong, Vinay Prabhu, Dominik Nickel, Simon Arberet, Hersh Chandarana, Krishna P. Shanbhogue
Publikováno v:
Abdom Radiol (NY)
PURPOSE: Fat-suppressed T2-weighted imaging (T2-FS) requires a long scan time and can be wrought with motion artifacts, urging the development of a shorter and more motion robust sequence. We compare the image quality of a single-shot T2-weighted MRI
Autor:
Pierre Wary, Gabriela Hossu, Khalid Ambarki, Dominik Nickel, Simon Arberet, Julien Oster, Xavier Orry, Valérie Laurent
Publikováno v:
European Radiology.
Autor:
Simon Arberet, Peter Speier, Mariappan S. Nadar, Gregor Körzdörfer, Mathias Nittka, Heiko Meyer, Boris Mailhe, Xiao Chen
Publikováno v:
Magnetic Resonance Imaging. 82:74-90
Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equa
Autor:
Dominik Nickel, Konstantin Nikolaou, Sebastian Gassenmaier, Simon Arberet, Judith Herrmann, Saif Afat, Ahmed E. Othman, John P. Mugler
Publikováno v:
Investigative Radiology. 56:645-652
Objective Deep learning (DL) reconstruction enables substantial acceleration of image acquisition while maintaining diagnostic image quality. The aims of this study were to overcome the drawback of specific absorption rate (SAR)-related limitations a
Autor:
Simon Arberet, Hersh Chandarana, Rebecca Anthopolos, Dominik Nickel, Paul Smereka, Angela Tong, Krishna Shanbhogue
Publikováno v:
European Radiology. 31:8447-8457
To compare the image quality of an accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction (DL HASTE-FS) with conventional T2-weighted FS sequence (conventional T2 FS) at 1.5 T.One hundre
Autor:
Sung Hwan Bae, Jiyoung Hwang, Seong Sook Hong, Eun Ji Lee, Jewon Jeong, Thomas Benkert, JaeKon Sung, Simon Arberet
Publikováno v:
European journal of radiology. 154
To assess the clinical feasibility of accelerated deep learning-reconstructed diffusion weighted imaging (DWI) and to compare its image quality and acquisition time with those of conventional DWI.Seventy-four consecutive patients who underwent 3 T ab
Autor:
Haidara Almansour, Judith Herrmann, Sebastian Gassenmaier, Andreas Lingg, Marcel Dominik Nickel, Stephan Kannengiesser, Simon Arberet, Ahmed E. Othman, Saif Afat
Publikováno v:
Academic radiology.
To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interpolated breath-hold examina
Autor:
Matthias Kündel, Dominik Nickel, Judith Herrmann, Andreas Lingg, Saif Afat, Sebastian Gassenmaier, Simon Arberet, Ahmed E. Othman
Publikováno v:
Investigative Radiology. 56:313-319
OBJECTIVE The aim of this study was to evaluate the feasibility of a single breath-hold fast half-Fourier single-shot turbo spin echo (HASTE) sequence using a deep learning reconstruction (HASTEDL) for T2-weighted magnetic resonance imaging of the ab
Autor:
Judith Herrmann, Daniel Wessling, Dominik Nickel, Simon Arberet, Haidara Almansour, Carmen Afat, Saif Afat, Sebastian Gassenmaier, Ahmed E. Othman
Publikováno v:
Academic radiology. 30(1)
To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE
Autor:
Simon Arberet, Sebastian Gassenmaier, Daniel Wessling, Saif Afat, Judith Herrmann, Carmen Afat, Ahmed E. Othman, Dominik Nickel
Publikováno v:
Investigative radiology. 57(3)
OBJECTIVES The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradien