Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …
diagnoses and research which underpin many recent breakthroughs in medicine and …
[HTML][HTML] Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of
radiation to tumors while sparing healthy tissues over multiple days. Computed tomography …
radiation to tumors while sparing healthy tissues over multiple days. Computed tomography …
Spatio-angular convolutions for super-resolution in diffusion mri
M Lyon, P Armitage, MA Álvarez - Advances in Neural …, 2024 - proceedings.neurips.cc
Diffusion MRI (dMRI) is a widely used imaging modality, but requires long scanning times to
acquire high resolution datasets. By leveraging the unique geometry present within this …
acquire high resolution datasets. By leveraging the unique geometry present within this …
Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI
Objective This study aims to assess the statistical significance of training parameters in 240
dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and …
dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and …
A deep learning approach to upscaling “low-quality” MR Images: an in silico comparison study based on the UNet framework
MR scans of low-gamma X-nuclei, low-concentration metabolites, or standard imaging at
very low field entail a challenging tradeoff between resolution, signal-to-noise, and …
very low field entail a challenging tradeoff between resolution, signal-to-noise, and …
Multi image super resolution of MRI images using generative adversarial network
U Nimitha, PM Ameer - Journal of Ambient Intelligence and Humanized …, 2024 - Springer
In recent decades, computer-aided medical image analysis has become a popular
techniques for disease detection and diagnosis. Deep learning-based image processing …
techniques for disease detection and diagnosis. Deep learning-based image processing …
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 …
Through-plane super-resolution with autoencoders in diffusion magnetic resonance imaging of the developing human brain
Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower
through-plane than in-plane resolution. This anisotropy is often overcome by classical …
through-plane than in-plane resolution. This anisotropy is often overcome by classical …
MRI super-resolution using similarity distance and multi-scale receptive field based feature fusion GAN and pre-trained slice interpolation network
U Nimitha, PM Ameer - Magnetic Resonance Imaging, 2024 - Elsevier
Challenges arise in achieving high-resolution Magnetic Resonance Imaging (MRI) to
improve disease diagnosis accuracy due to limitations in hardware, patient discomfort, long …
improve disease diagnosis accuracy due to limitations in hardware, patient discomfort, long …
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 …