AIMC Topic: Radiographic Image Enhancement

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A deep-learning model for identifying fresh vertebral compression fractures on digital radiography.

European radiology Sep 22, 2021
OBJECTIVES: To develop a deep-learning (DL) model for identifying fresh VCFs from digital radiography (DR), with magnetic resonance imaging (MRI) as the reference standard.
Retrospective Studies Deep Learning Spinal Fractures Fractures, Compression Radiographic Image Enhancement Humans
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A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis.

Scientific reports May 13, 2021
Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in ...
Computer Simulation Female Humans Breast Neoplasms Neural Networks, Computer Middle Aged Radiographic Image Interpretation, Computer-Assisted Mammography Breast Radiographic Image Enhancement
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Deep-learning-based image reconstruction in dynamic contrast-enhanced abdominal CT: image quality and lesion detection among reconstruction strength levels.

Clinical radiology Apr 18, 2021
AIM: To evaluate the use of deep-learning-based image reconstruction (DLIR) algorithms in dynamic contrast-enhanced computed tomography (CT) of the abdomen, and to compare the image quality and lesion conspicuity among the reconstruction strength lev...
Radiographic Image Enhancement Radiography, Abdominal Aged Humans Aged, 80 and over Prospective Studies Contrast Media Middle Aged Adult Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Deep Learning Female Abdominal Neoplasms Male
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Deep Learning-Based Computed Tomography Perfusion Mapping (DL-CTPM) for Pulmonary CT-to-Perfusion Translation.

International journal of radiation oncology, biology, physics Mar 6, 2021
PURPOSE: Our purpose was to develop a deep learning-based computed tomography (CT) perfusion mapping (DL-CTPM) method that synthesizes lung perfusion images from CT images.
Humans Neural Networks, Computer Male Lung Deep Learning Radiopharmaceuticals Statistics, Nonparametric Retrospective Studies Tomography, X-Ray Computed Female Aged Tomography, Emission-Computed, Single-Photon Perfusion Imaging Radiographic Image Enhancement Technetium Tc 99m Aggregated Albumin
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A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions.

The British journal of radiology Dec 11, 2020
OBJECTIVE: To investigate the feasibility of using deep learning image reconstruction (DLIR) to significantly reduce radiation dose and improve image quality in contrast-enhanced abdominal CT.
Radiation Dosage Radiographic Image Enhancement Liver Neoplasms Contrast Media Liver Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Female Reproducibility of Results Aged Male Middle Aged Deep Learning Aged, 80 and over Adult Humans Prospective Studies
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Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms.

Computational and mathematical methods in medicine Oct 28, 2020
The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast cancer among women in 2020. Early detection of breast cancer, followed by appropriate treatment, can re...
Radiographic Image Interpretation, Computer-Assisted Radiographic Image Enhancement Breast Neoplasms Deep Learning Computational Biology Humans Mammography Neural Networks, Computer Databases, Factual Female
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Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs.

JAMA network open Sep 1, 2020
IMPORTANCE: The improvement of pulmonary nodule detection, which is a challenging task when using chest radiographs, may help to elevate the role of chest radiographs for the diagnosis of lung cancer.
Radiography, Thoracic Radiographic Image Enhancement Early Detection of Cancer Predictive Value of Tests Radiologists Tumor Burden Reproducibility of Results Aged Humans Sensitivity and Specificity Artificial Intelligence Male Algorithms Middle Aged Deep Learning Female Lung Lung Neoplasms Solitary Pulmonary Nodule
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The effect of deep convolutional neural networks on radiologists' performance in the detection of hip fractures on digital pelvic radiographs.

European journal of radiology Jul 23, 2020
PURPOSE: The purpose of our study is to develop deep convolutional neural network (DCNN) for detecting hip fractures using CT and MRI as a gold standard, and to evaluate the diagnostic performance of 7 readers with and without DCNN.
Aged Aged, 80 and over Male Middle Aged Image Processing, Computer-Assisted Magnetic Resonance Imaging Area Under Curve Deep Learning ROC Curve Humans Neural Networks, Computer Tomography, X-Ray Computed Female Pelvis Hip Fractures Radiographic Image Enhancement Radiography, Abdominal
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Optical techniques, computed tomography and deep learning role in the diagnosis of COVID-19 pandemic towards increasing the survival rate of vulnerable populations.

Photodiagnosis and photodynamic therapy Jun 17, 2020
• Severe lung complications can be explored using computed tomography during COVID-19 pandemic. • Ultra-low dose CT can enhance COVID-19 infected patients diagnostic capability. • Optically monitored CT along with deep learning is the best solution f...
Survival Analysis Quality Improvement Role Radiographic Image Enhancement Deep Learning Diagnostic Imaging Coronavirus Infections COVID-19 Testing Humans Pandemics Optical Devices COVID-19 Clinical Laboratory Techniques Severe Acute Respiratory Syndrome Female Tomography, X-Ray Computed Pneumonia, Viral Male Vulnerable Populations
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Machine learning paradigm for dynamic contrast-enhanced MRI evaluation of expanding bladder.

Frontiers in bioscience (Landmark edition) Jun 1, 2020
Delineation of the bladder under a dynamic contrast enhanced (DCE)-MRI protocol requires robust segmentation. However, this method is subject to errors due to variations in the content of fluid within the bladder, as well as presence of air and simil...
Contrast Media Magnetic Resonance Imaging Urinary Bladder Radiographic Image Enhancement Humans Neural Networks, Computer Machine Learning Algorithms Reproducibility of Results
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