AIMC Topic: Image Enhancement

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PET image denoising using unsupervised deep learning.

European journal of nuclear medicine and molecular imaging Aug 29, 2019
PURPOSE: Image quality of positron emission tomography (PET) is limited by various physical degradation factors. Our study aims to perform PET image denoising by utilizing prior information from the same patient. The proposed method is based on unsup...
Humans Image Enhancement Signal-To-Noise Ratio Phantoms, Imaging Unsupervised Machine Learning Adult Female Deep Learning Positron-Emission Tomography Male Aged Quality Control Image Processing, Computer-Assisted Middle Aged Aged, 80 and over Lung Neoplasms
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An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

Physics in medicine and biology Aug 21, 2019
Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SNR), subsequently affecting diagnostic and quantitative accuracies. Deep learning methods have shown ...
Humans Algorithms Neoplasms Image Enhancement Deep Learning Signal-To-Noise Ratio Image Processing, Computer-Assisted Normal Distribution Positron-Emission Tomography
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Multiscale brain MRI super-resolution using deep 3D convolutional networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Aug 14, 2019
The purpose of super-resolution approaches is to overcome the hardware limitations and the clinical requirements of imaging procedures by reconstructing high-resolution images from low-resolution acquisitions using post-processing methods. Super-reso...
Brain Mapping Datasets as Topic Image Enhancement Humans Neural Networks, Computer Imaging, Three-Dimensional Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Accurate automated Cobb angles estimation using multi-view extrapolation net.

Medical image analysis Aug 9, 2019
Accurate automated quantitative Cobb angle estimation that quantitatively evaluates scoliosis plays an important role in scoliosis diagnosis and treatment. It solves the problem of the traditional manual method, which is the current clinical standard...
Humans Deep Learning Child Image Enhancement Scoliosis Anatomic Landmarks Radiographic Image Interpretation, Computer-Assisted Datasets as Topic
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k-Space deep learning for reference-free EPI ghost correction.

Magnetic resonance in medicine Jul 18, 2019
PURPOSE: Nyquist ghost artifacts in echo planar imaging (EPI) are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field ...
Humans Neural Networks, Computer Algorithms Brain Image Enhancement Models, Theoretical Signal-To-Noise Ratio Magnetic Resonance Imaging Phantoms, Imaging Reproducibility of Results Radionuclide Imaging Artifacts Echo-Planar Imaging Deep Learning Image Processing, Computer-Assisted
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A convolutional neural network for ultra-low-dose CT denoising and emphysema screening.

Medical physics Jul 17, 2019
PURPOSE: Reducing dose level to achieve ALARA is an important task in diagnostic and therapeutic applications of computed tomography (CT) imaging. Effective image quality enhancement strategies are crucial to compensate for the degradation caused by ...
Signal-To-Noise Ratio Pulmonary Emphysema Image Enhancement Radiation Dosage Tomography, X-Ray Computed Lung Image Processing, Computer-Assisted Neural Networks, Computer
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Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm.

Neural networks : the official journal of the International Neural Network Society Jul 15, 2019
Accounting for the morphology of shale formations, which represent highly heterogeneous porous media, is a difficult problem. Although two- or three-dimensional images of such formations may be obtained and analyzed, they either do not capture the na...
Image Enhancement Stochastic Processes Deep Learning Imaging, Three-Dimensional
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Cell mitosis event analysis in phase contrast microscopy images using deep learning.

Medical image analysis Jun 22, 2019
In this paper, we solve the problem of mitosis event localization and its stage localization in time-lapse phase-contrast microscopy images. Our method contains three steps: first, we formulate a Low-Rank Matrix Recovery (LRMR) model to find salient ...
Deep Learning Mesenchymal Stem Cells Artifacts Cell Tracking Image Enhancement Neural Networks, Computer Mitosis Microscopy, Phase-Contrast
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Extraction of the cardiac waveform from simultaneous multislice fMRI data using slice sorted averaging and a deep learning reconstruction filter.

NeuroImage May 23, 2019
Cardiac signal contamination has long confounded the analysis of blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). Cardiac pulsation results in significant BOLD signal changes, especially in and around blood vesse...
Magnetic Resonance Imaging Young Adult Humans Artifacts Brain Mapping Algorithms Adult Image Enhancement Heart Rate Brain Deep Learning
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Endoscopic Surveillance of Barrett's Esophagus Using Volumetric Laser Endomicroscopy With Artificial Intelligence Image Enhancement.

Gastroenterology May 9, 2019
Esophageal Neoplasms Humans Barrett Esophagus Artificial Intelligence Image Enhancement Esophagoscopy Disease Progression Esophageal Mucosa Watchful Waiting
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