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 …
Deep learning-based magnetic resonance image super-resolution: a survey
Magnetic resonance imaging (MRI) is a medical imaging technique used to show
anatomical structures and physiological processes of the human body. Due to limitations like …
anatomical structures and physiological processes of the human body. Due to limitations like …
Arbitrary scale super-resolution diffusion model for brain MRI images
Z Han, W Huang - Computers in Biology and Medicine, 2024 - Elsevier
Given the constraints posed by hardware capacity, scan duration, and patient cooperation,
the reconstruction of magnetic resonance imaging (MRI) images emerges as a pivotal …
the reconstruction of magnetic resonance imaging (MRI) images emerges as a pivotal …
Cross-modality cerebrovascular segmentation based on pseudo-label generation via paired data
Accurate segmentation of cerebrovascular structures from Computed Tomography
Angiography (CTA), Magnetic Resonance Angiography (MRA), and Digital Subtraction …
Angiography (CTA), Magnetic Resonance Angiography (MRA), and Digital Subtraction …
A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network …
Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns
associated with real medical data. An approach that stands out to circumvent this hurdle is …
associated with real medical data. An approach that stands out to circumvent this hurdle is …
Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction
G Gohla, TK Hauser, P Bombach, D Feucht, A Estler… - Cancers, 2024 - mdpi.com
Simple Summary Interest in applying artificial intelligence to medical imaging to enhance
image quality has grown in both clinical practice and research. Nonetheless, these artificial …
image quality has grown in both clinical practice and research. Nonetheless, these artificial …
A Framework for Reconstructing Super-Resolution Magnetic Resonance Images from Sparse Raw Data Using Multilevel Generative Methods
K Malczewski - Applied Sciences, 2024 - mdpi.com
Super-resolution magnetic resonance (MR) scans give anatomical data for quantitative
analysis and treatment. The use of convolutional neural networks (CNNs) in image …
analysis and treatment. The use of convolutional neural networks (CNNs) in image …
[HTML][HTML] Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved …
M Jurka, I Macova, M Wagnerova… - … Imaging in Medicine …, 2024 - ncbi.nlm.nih.gov
Background Deep-learning-based reconstruction (DLR) improves the quality of magnetic
resonance (MR) images which allows faster acquisitions. The aim of this study was to …
resonance (MR) images which allows faster acquisitions. The aim of this study was to …
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models
X Wang, S López-Tapia, A Lucas, X Wu… - arXiv preprint arXiv …, 2024 - arxiv.org
Generative Adversarial Networks (GANs) have shown great performance on super-
resolution problems since they can generate more visually realistic images and video …
resolution problems since they can generate more visually realistic images and video …
Bridging Modalities with VarVit-GAN: A Generative Adversarial Network for Multi-Modal Brain MRI Translation
In the realm of medical imaging, capturing diverse aspects of brain structures is crucial for
accurate diagnoses of brain tumors. Magnetic Resonance Imaging (MRI) has emerged as …
accurate diagnoses of brain tumors. Magnetic Resonance Imaging (MRI) has emerged as …