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Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide versatile
information. However, it has a fundamental challenge that is time consuming to acquire …
information. However, it has a fundamental challenge that is time consuming to acquire …
MRI-guidance for motion management in external beam radiotherapy: current status and future challenges
High precision conformal radiotherapy requires sophisticated imaging techniques to aid in
target localisation for planning and treatment, particularly when organ motion due to …
target localisation for planning and treatment, particularly when organ motion due to …
Accelerating magnetic resonance imaging via deep learning
This paper proposes a deep learning approach for accelerating magnetic resonance
imaging (MRI) using a large number of existing high quality MR images as the training …
imaging (MRI) using a large number of existing high quality MR images as the training …
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
This paper proposes a multi-channel image reconstruction method, named
DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional …
DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional …
Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine
B Tang, T Song, F Li, L Deng - Renewable Energy, 2014 - Elsevier
Fault diagnosis for wind turbine transmission systems is an important task for reducing their
maintenance cost. However, the non-stationary dynamic operating conditions of wind …
maintenance cost. However, the non-stationary dynamic operating conditions of wind …
Direct shape regression networks for end-to-end face alignment
Face alignment has been extensively studied in computer vision community due to its
fundamental role in facial analysis, but it remains an unsolved problem. The major …
fundamental role in facial analysis, but it remains an unsolved problem. The major …
Segmentation of ultrasound image sequences by combing a novel deep siamese network with a deformable contour model
Deformable contours are widely applied in medical image segmentation, which are usually
derived from appearance cues in medical images. However, the performance of deformed …
derived from appearance cues in medical images. However, the performance of deformed …
[HTML][HTML] McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction
Deep learning MRI reconstruction methods are often based on Convolutional neural
network (CNN) models; however, they are limited in capturing global correlations among …
network (CNN) models; however, they are limited in capturing global correlations among …
Liver 4DMRI: a retrospective image‐based sorting method
Purpose: Four‐dimensional magnetic resonance imaging (4DMRI) is an emerging
technique in radiotherapy treatment planning for organ motion quantification. In this paper …
technique in radiotherapy treatment planning for organ motion quantification. In this paper …
PET respiratory motion correction: quo vadis?
Positron emission tomography (PET) respiratory motion correction has been a subject of
great interest for the last twenty years, prompted mainly by the development of multimodality …
great interest for the last twenty years, prompted mainly by the development of multimodality …