Analysis of a deep learning-based superresolution algorithm tailored to partial fourier gradient echo sequences of the abdomen at 1.5 T: reduction of breath-hold time …
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 …
learning superresolution algorithm tailored to partial Fourier allowing retrospectively …
Combined deep learning-based super-resolution and partial fourier reconstruction for gradient echo sequences in abdominal MRI at 3 Tesla: shortening breath-hold …
H Almansour, J Herrmann, S Gassenmaier, A Lingg… - Academic radiology, 2023 - Elsevier
Rationale and Objectives To investigate the impact of a prototypical deep learning–based
super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on …
super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on …
Deep learning-based superresolution reconstruction for upper abdominal magnetic resonance imaging: an analysis of image quality, diagnostic confidence, and …
H Almansour, S Gassenmaier, D Nickel… - Investigative …, 2021 - journals.lww.com
Objectives The aim of this study was to investigate the impact of a deep learning-based
superresolution reconstruction technique for T1-weighted volume-interpolated breath-hold …
superresolution reconstruction technique for T1-weighted volume-interpolated breath-hold …
Application of a deep learning algorithm for combined super-resolution and partial fourier reconstruction including time reduction in T1-weighted precontrast and …
Purpose: The purpose of this study was to test the technical feasibility and the impact on the
image quality of a deep learning-based super-resolution reconstruction algorithm in 1.5 T …
image quality of a deep learning-based super-resolution reconstruction algorithm in 1.5 T …
Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest
S Maennlin, D Wessling, J Herrmann, H Almansour… - La radiologia …, 2023 - Springer
Objectives A deep learning-based super-resolution for postcontrast volume-interpolated
breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to …
breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to …
[HTML][HTML] Deep learning-based super-resolution gradient echo imaging of the pancreas: Improvement of image quality and reduction of acquisition time
Purpose The purpose of this study was to evaluate the impact of a deep learning-based
super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric …
super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric …
Deep CNN-based ultrasound super-resolution for high-speed high-resolution B-mode imaging
W Choi, M Kim, J HakLee, J Kim… - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
In real-time high-resolution B-mode ultrasound (US) imaging, the lateral resolution, or the
number of scan lines, may be limited due to the speed of sound, if a longer penetration …
number of scan lines, may be limited due to the speed of sound, if a longer penetration …
Correction to: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.
M Akagi, Y Nakamura, T Higaki, K Narita… - European …, 2019 - europepmc.org
Correction to: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution
CT. - Abstract - Europe PMC Sign in | Create an account https://orcid.org Europe PMC Menu …
CT. - Abstract - Europe PMC Sign in | Create an account https://orcid.org Europe PMC Menu …
Deep 3D convolutional neural networks for fast super-resolution ultrasound imaging
Super-resolution ultrasound imaging (SR-US) is a new technique which breaks the
diffraction limit and can help visualize microvascularity at a resolution of tens of microns …
diffraction limit and can help visualize microvascularity at a resolution of tens of microns …
Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
M Akagi, Y Nakamura, T Higaki, K Narita, Y Honda… - European …, 2019 - Springer
Objectives Deep learning reconstruction (DLR) is a new reconstruction method; it introduces
deep convolutional neural networks into the reconstruction flow. This study was conducted …
deep convolutional neural networks into the reconstruction flow. This study was conducted …
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