[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 …
[HTML][HTML] Recent Applications of Explainable AI (XAI): A Systematic Literature Review
M Saarela, V Podgorelec - Applied Sciences, 2024 - mdpi.com
This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …
[HTML][HTML] Effective deep-learning brain MRI super resolution using simulated training data
Background: In the field of medical imaging, high-resolution (HR) magnetic resonance
imaging (MRI) is essential for accurate disease diagnosis and analysis. However, HR …
imaging (MRI) is essential for accurate disease diagnosis and analysis. However, HR …
[HTML][HTML] Multi-contrast high-field quality image synthesis for portable low-field MRI using generative adversarial networks and paired data
Methods: A total of 49 MS patients were scanned on portable 64mT and standard 3T
scanners at Penn (n= 25) or the National Institutes of Health (NIH, n= 24) with T1-weighted …
scanners at Penn (n= 25) or the National Institutes of Health (NIH, n= 24) with T1-weighted …
Mouse brain MR super-resolution using a deep learning network trained with optical imaging data
Introduction The resolution of magnetic resonance imaging is often limited at the millimeter
level due to its inherent signal-to-noise disadvantage compared to other imaging modalities …
level due to its inherent signal-to-noise disadvantage compared to other imaging modalities …
Super-resolution of brain MRI via U-Net architecture
A Kalluvila - … Journal of Advanced Computer Science and …, 2023 - search.proquest.com
This paper proposes a U-Net-based deep learning architecture for the task of super-
resolution of lower resolution brain magnetic resonance images (MRI). The proposed …
resolution of lower resolution brain magnetic resonance images (MRI). The proposed …
Super-Resolution MRH Reconstruction for Mouse Models
Abstract Magnetic Resonance Imaging (MRI) has been widely used in pathology research
and plays a vital role in clinical diagnoses. However, obtaining high-resolution MRI stays a …
and plays a vital role in clinical diagnoses. However, obtaining high-resolution MRI stays a …
Deep learning segmentation of low-resolution images for prostate magnetic resonance-guided radiotherapy
The MR-Linac can enable real-time radiotherapy adaptation. However, real-time image
acquisition is restricted to 2D to obtain sufficient spatial resolution, hindering accurate 3D …
acquisition is restricted to 2D to obtain sufficient spatial resolution, hindering accurate 3D …
Machine Learning in Magnetic Resonance-Guided Adaptive Radiotherapy
S Fransson - 2024 - diva-portal.org
Background and Purpose: Treatments on combined Magnetic Resonance (MR) scanners
and Linear Accelerators (Linacs) for radiotherapy, called MR-Linacs, often require daily …
and Linear Accelerators (Linacs) for radiotherapy, called MR-Linacs, often require daily …
[PDF][PDF] Advances in Machine Learning for Magnetic Resonance Imaging of Acute Ischemic Stroke: A Systematic Review
S Azamat, E Gürkaş, E Ozturk-Isik - 2024 - curremr.com
Stroke demands rapid and precise diagnosis. Recent advancements in machine learning
(ML) have facilitated its integration with magnetic resonance imaging (MRI) for assessing …
(ML) have facilitated its integration with magnetic resonance imaging (MRI) for assessing …