Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in …
Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since …
Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision …
Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based …
Image reconstruction from undersampled k-space data has been playing an important role in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated …
Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has …
M Ran, W Xia, Y Huang, Z Lu, P Bao… - … on Radiation and …, 2020 - ieeexplore.ieee.org
Compressed sensing magnetic resonance imaging (CS-MRI) is a theoretical framework that can accurately reconstruct images from undersampled k-space data with a much lower …
M Jiang, M Zhi, L Wei, X Yang, J Zhang, Y Li… - … Medical Imaging and …, 2021 - Elsevier
High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time. In this paper, a …
S Wang, H Cheng, L Ying, T Xiao, Z Ke, H Zheng… - Magnetic resonance …, 2020 - Elsevier
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional …