[HTML][HTML] Medical image super-resolution for smart healthcare applications: A comprehensive survey
The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …
models and the Internet of Things (IoT), is creating unprecedented opportunities for …
Photoacoustic imaging aided with deep learning: a review
Photoacoustic imaging (PAI) is an emerging hybrid imaging modality integrating the benefits
of both optical and ultrasound imaging. Although PAI exhibits superior imaging capabilities …
of both optical and ultrasound imaging. Although PAI exhibits superior imaging capabilities …
TranSMS: Transformers for super-resolution calibration in magnetic particle imaging
Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles
(MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a …
(MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a …
[HTML][HTML] SOUP-GAN: Super-resolution MRI using generative adversarial networks
There is a growing demand for high-resolution (HR) medical images for both clinical and
research applications. Image quality is inevitably traded off with acquisition time, which in …
research applications. Image quality is inevitably traded off with acquisition time, which in …
CuNeRF: Cube-based neural radiance field for zero-shot medical image arbitrary-scale super resolution
Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread
attention, aiming to supersample medical volumes at arbitrary scales via a single model …
attention, aiming to supersample medical volumes at arbitrary scales via a single model …
Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography
Y Nagayama, T Emoto, Y Kato, M Kidoh, S Oda… - European …, 2023 - Springer
Objectives To evaluate the effect of super-resolution deep-learning-based reconstruction
(SR-DLR) on the image quality of coronary CT angiography (CCTA). Methods Forty-one …
(SR-DLR) on the image quality of coronary CT angiography (CCTA). Methods Forty-one …
Deep learning super-resolution reconstruction for fast and motion-robust T2-weighted prostate MRI
Background Deep learning (DL) reconstructions can enhance image quality while
decreasing MRI acquisition time. However, DL reconstruction methods combined with …
decreasing MRI acquisition time. However, DL reconstruction methods combined with …
Coronary stent evaluation by CTA: image quality comparison between super-resolution deep learning reconstruction and other reconstruction algorithms
Y Nagayama, T Emoto, H Hayashi… - American Journal of …, 2023 - Am Roentgen Ray Soc
BACKGROUND. A super-resolution deep learning reconstruction (SR-DLR) algorithm may
provide better image sharpness than earlier reconstruction algorithms and thereby improve …
provide better image sharpness than earlier reconstruction algorithms and thereby improve …
Machine learning in magnetic resonance imaging: image reconstruction
Abstract Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management
and monitoring of many diseases. However, it is an inherently slow imaging technique. Over …
and monitoring of many diseases. However, it is an inherently slow imaging technique. Over …
Reconstructing high fidelity digital rock images using deep convolutional neural networks
Imaging methods have broad applications in geosciences. Scanning electron microscopy
(SEM) and micro-CT scanning have been applied for studying various geological problems …
(SEM) and micro-CT scanning have been applied for studying various geological problems …