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Image Enhancement

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Image Quality Enhancement Using a Deep Neural Network for Plane Wave Medical Ultrasound Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Plane wave imaging (PWI), a typical ultrafast medical ultrasound imaging mode, adopts single plane wave emission without focusing to achieve a high frame rate. However, the imaging quality is severely degraded in comparison with the commonly used foc...

Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Differentiating glioblastoma from solitary brain metastasis preoperatively using conventional MR images is challenging. Deep learning models have shown promise in performing classification tasks. The diagnostic performance of ...

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images.

Computers in biology and medicine
Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has se...

Improved Quantification of Myocardium Scar in Late Gadolinium Enhancement Images: Deep Learning Based Image Fusion Approach.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Quantification of myocardium scarring in late gadolinium enhanced (LGE) cardiac magnetic resonance imaging can be challenging due to low scar-to-background contrast and low image quality. To resolve ambiguous LGE regions, experienced read...

Neural network enhanced 3D turbo spin echo for MR intracranial vessel wall imaging.

Magnetic resonance imaging
PURPOSE: To improve the signal-to-noise ratio (SNR) and image sharpness for whole brain isotropic 0.5 mm three-dimensional (3D) T weighted (Tw) turbo spin echo (TSE) intracranial vessel wall imaging (IVWI) at 3 T.

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement.

Sensors (Basel, Switzerland)
BACKGROUND: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundari...

Reference-Driven Undersampled MR Image Reconstruction Using Wavelet Sparsity-Constrained Deep Image Prior.

Computational and mathematical methods in medicine
Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, hig...

Improvement of late gadolinium enhancement image quality using a deep learning-based reconstruction algorithm and its influence on myocardial scar quantification.

European radiology
OBJECTIVES: The aim of this study was to assess the effect of a deep learning (DL)-based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification.

Artificial Intelligence Applied to Breast MRI for Improved Diagnosis.

Radiology
Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient tr...

Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI.

NeuroImage
Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, most existing approaches are based on deterministic models, neglecting the presence of different source...