MDCT

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Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in abdomen dual-energy CT

European Radiology January 2023

The aim of this study was to investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to other reconstruction algorithms. The authors showed that the DLIR algorithm reduced image noise and variability of iodine concentration values when compared with other reconstruction algorithms. Key points In the

Artificial neural network detects contrast phase in MDCT

Sebastian Rühling February 2022

Automated extraction of novel biomarkers from routine imaging examinations, e.g., opportunistic CT, is a promising opportunity to extend current screening possibilities and to better guide individualized therapies. In theory, opportunistic CT can be used to obtain quantitative biomarker data from any tissue included in a routine examination. Over the past decade, AI has been used to develop many different automated

Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT

European Radiology July 2021

The authors of this study aimed to develop and validate a combined radiomics-clinical model to predict malignancy of cerebral compression fractures on CT. This study comprised 165 patients with vertebral compression fractures who were allocated to training and validation cohorts. The authors were able to determine that the combined radiomics-clinical model integrating clinical parameters with radiomics score could predict malignancy

Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning

Christian Tesche December 2020

In recent years, artificial intelligence (AI) and, in particular, the application of machine learning (ML) algorithms, have become a new cornerstone in cardiovascular imaging with improved decision pathways, risk stratification, and outcome prediction in a more objective, reproducible, and rational manner. The integration of ML in daily routine clinical practice may hold potential to improve imaging workflow and to promote

Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics

European Radiology December 2020

The purpose of this single-center retrospective study was to investigate the effectiveness of contrast-enhanced computed tomography (CECT)-based radiomic signatures for the preoperative prediction of pathological grades of hepatocellular carcinoma (HCC) via machine learning. The authors found that the radiomics signatures could non-invasively explore the underlying association between CECT images and pathological grades of HCC. Key points The radiomics signatures may

Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

European Radiology January 2020

The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure above 15 mmHg, which was based on pre-operative cardiac CT. The study included 96 patients who underwent a bidirectional Glenn procedure. Key points Twenty-three candidate descriptors were manually extracted from

Evaluation of an AI-based, automatic coronary artery calcium scoring software

European Radiology January 2020

In this study, using a semi-automatic software as a reference, the authors aimed to evaluate an artificial intelligence (AI)-based, automatic coronary artery calcium (CAC) scoring software. This observational study included 315 non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. The authors determined that there was a strong correlation between the automatic software and the semi-automatic software for three CAC scores and

Noncontrast computer tomography–based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model

European Radiology October 2019

The authors developed a radiomics model for predicting hematoma expansion in patients with intracerebral haemorrhage (ICH) and compared its predictive performance with a conventional radiological feature-based model. Through retrospective analysis and noncontrast computed tomography (NCCT) assessment, it was found that an NCCT-based radiomics model showed better performance in the prediction of early hematoma expansion in ICH patients. Key points Radiomics

Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer

European Radiology October 2019

The authors of this study constructed two multivariate logistic regression models and compared the diagnostic performance between the two of them via receiver operating characteristic analysis, discovering that CT radiomics analysis can provide valuable information for predicting occult peritoneal metastases in advanced gastric cancer. Key points Venous CT radiomics analysis provided valuable information for predicting occult peritoneal metastases in advanced

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in abdomen dual-energy CT

European Radiology January 2023

Artificial neural network detects contrast phase in MDCT

Sebastian Rühling, Fernando Navarro February 2022

Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT

European Radiology July 2021

Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning

Christian Tesche December 2020

Noncontrast computer tomography–based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model

European Radiology October 2019

Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer

European Radiology October 2019

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

Latest posts

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in abdomen dual-energy CT

Artificial neural network detects contrast phase in MDCT

Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT

Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning

Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics

Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

Evaluation of an AI-based, automatic coronary artery calcium scoring software

Noncontrast computer tomography–based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model

Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in abdomen dual-energy CT

Artificial neural network detects contrast phase in MDCT

Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT

Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning

Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics

Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

Evaluation of an AI-based, automatic coronary artery calcium scoring software

Noncontrast computer tomography–based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model

Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer

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Welcome to the blog on Artificial Intelligence ofthe European Society of Radiology

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Check out our different membership options.

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Footnotes:

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology