AIMC Topic: 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...
Prospective Studies Tomography, X-Ray Computed Female Aged Aged, 80 and over Humans Contrast Media Adult Radiographic Image Interpretation, Computer-Assisted Male Abdominal Neoplasms Middle Aged Radiographic Image Enhancement Deep Learning Radiography, Abdominal
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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.
Perfusion Imaging Radiographic Image Enhancement Technetium Tc 99m Aggregated Albumin Tomography, X-Ray Computed Lung Female Humans Deep Learning Aged Neural Networks, Computer Radiopharmaceuticals Male Retrospective Studies Statistics, Nonparametric Tomography, Emission-Computed, Single-Photon
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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.
Humans Prospective Studies Aged, 80 and over Adult Male Middle Aged Deep Learning Tomography, X-Ray Computed Female Reproducibility of Results Aged Radiation Dosage Radiographic Image Enhancement Liver Neoplasms Contrast Media Liver Radiographic Image Interpretation, Computer-Assisted
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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 Humans Deep Learning Neural Networks, Computer Computational Biology Databases, Factual Mammography 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.
Tumor Burden Radiography, Thoracic Radiographic Image Enhancement Solitary Pulmonary Nodule Early Detection of Cancer Predictive Value of Tests Radiologists Female Reproducibility of Results Aged Sensitivity and Specificity Humans Male Artificial Intelligence Algorithms Middle Aged Lung Deep Learning Lung Neoplasms
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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.
Radiography, Abdominal Female Area Under Curve Aged Pelvis Humans Middle Aged Aged, 80 and over Hip Fractures Neural Networks, Computer Deep Learning Male Radiographic Image Enhancement Tomography, X-Ray Computed ROC Curve Image Processing, Computer-Assisted Magnetic Resonance Imaging
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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...
Role Radiographic Image Enhancement Optical Devices Clinical Laboratory Techniques Severe Acute Respiratory Syndrome Pneumonia, Viral Coronavirus Infections Humans COVID-19 Testing Pandemics Survival Analysis COVID-19 Quality Improvement Male Tomography, X-Ray Computed Female Vulnerable Populations Deep Learning Diagnostic Imaging
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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...
Humans Neural Networks, Computer Machine Learning Urinary Bladder Algorithms Radiographic Image Enhancement Reproducibility of Results Contrast Media Magnetic Resonance Imaging
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Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography.

Occupational and environmental medicine May 29, 2020
OBJECTIVES: To investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.
Deep Learning China ROC Curve Dust Occupational Exposure Humans Reproducibility of Results Radiologists Pneumoconiosis Neural Networks, Computer Aged Radiography, Thoracic Retrospective Studies Male Radiographic Image Enhancement Female Middle Aged
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Learning deformable registration of medical images with anatomical constraints.

Neural networks : the official journal of the International Neural Network Society Jan 30, 2020
Deformable image registration is a fundamental problem in the field of medical image analysis. During the last years, we have witnessed the advent of deep learning-based image registration methods which achieve state-of-the-art performance, and drast...
Humans Deep Learning Radiographic Image Enhancement
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