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 …
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge
Dynamic imaging is a beneficial tool for interventions to assess physiological changes.
Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial …
Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial …
Multimodal-boost: Multimodal medical image super-resolution using multi-attention network with wavelet transform
Multimodal medical images are widely used by clinicians and physicians to analyze and
retrieve complementary information from high-resolution images in a non-invasive manner …
retrieve complementary information from high-resolution images in a non-invasive manner …
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning
Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the
microstructure of the nervous tissue, eg to delineate brain white matter connections in a non …
microstructure of the nervous tissue, eg to delineate brain white matter connections in a non …
Single image super-resolution methods: A survey
BC Maral - arXiv preprint arXiv:2202.11763, 2022 - arxiv.org
Super-resolution (SR), the process of obtaining high-resolution images from one or more
low-resolution observations of the same scene, has been a very popular topic of research in …
low-resolution observations of the same scene, has been a very popular topic of research in …
Ddos-unet: Incorporating temporal information using dynamic dual-channel unet for enhancing super-resolution of dynamic mri
Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue
contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for …
contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for …
3D isotropic super-resolution prostate MRI using generative adversarial networks and unpaired multiplane slices
We developed a deep learning–based super-resolution model for prostate MRI. 2D T2-
weighted turbo spin echo (T2w-TSE) images are the core anatomical sequences in a …
weighted turbo spin echo (T2w-TSE) images are the core anatomical sequences in a …
DE-Net: Detail-enhanced MR reconstruction network via global-local dependent attention
J Zhu, D Hu, W Mao, J Zhu, R Hu, Y Chen - Biomedical Signal Processing …, 2024 - Elsevier
Deep learning (DL) is widely used for MRI reconstruction and leverages significant
promotion. However, the existing DL-based methods still have some weaknesses. First, the …
promotion. However, the existing DL-based methods still have some weaknesses. First, the …
Self‐supervised multicontrast super‐resolution for diffusion‐weighted prostate MRI
B Gundogdu, M Medved, A Chatterjee… - Magnetic …, 2024 - Wiley Online Library
Purpose This study addresses the challenge of low resolution and signal‐to‐noise ratio
(SNR) in diffusion‐weighted images (DWI), which are pivotal for cancer detection …
(SNR) in diffusion‐weighted images (DWI), which are pivotal for cancer detection …
Rock CT image super-resolution using residual dual-channel attention generative adversarial network
Because of its benefits in terms of high speed, non-destructiveness, and three-
dimensionality, as well as ease of integration with computer simulation, computed …
dimensionality, as well as ease of integration with computer simulation, computed …