AIMC Topic: Contrast Media
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Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy.
Circulation
Jul 7, 2021
BACKGROUND: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but requires intravenous contrast agent administration. It is highly desired to deve...
How does artificial intelligence in radiology improve efficiency and health outcomes?
Pediatric radiology
Jun 12, 2021
Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a ...
A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification.
The British journal of radiology
Jun 11, 2021
OBJECTIVES: To compare the diagnostic performance of a newly developed artificial intelligence (AI) algorithm derived from the fusion of convolution neural networks (CNN) versus human observers in the estimation of malignancy risk in pulmonary nodule...
Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type.
Scientific reports
May 10, 2021
Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic var...
Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound.
Journal of gastroenterology and hepatology
May 5, 2021
BACKGROUND AND AIM: This study aims to construct a strategy that uses assistance from artificial intelligence (AI) to assist radiologists in the identification of malignant versus benign focal liver lesions (FLLs) using contrast-enhanced ultrasound (...
Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
Clinical radiology
May 1, 2021
AIM: To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma.
Contrast Media
Magnetic Resonance Imaging
Diagnosis, Differential
Predictive Value of Tests
Male
Middle Aged
Adolescent
Treatment Outcome
Glioblastoma
Image Interpretation, Computer-Assisted
Image Enhancement
Chemoradiotherapy
Retrospective Studies
Female
Reproducibility of Results
Aged
Young Adult
Adult
Sensitivity and Specificity
Brain Neoplasms
Humans
Machine Learning
Brain
Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study.
European journal of radiology
Apr 24, 2021
PURPOSE: Dual-source (DS) CT, dual-energy (DE) field of view (FoV) is limited to the size of the smaller detector array. The purpose was to establish a deep learning-based approach to DE extrapolation by estimating missing image data using data from ...
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...
Feasibility of automatic detection of small hepatocellular carcinoma (≤2 cm) in cirrhotic liver based on pattern matching and deep learning.
Physics in medicine and biology
Apr 16, 2021
Early detection of hepatocellular carcinoma (HCC) is crucial for clinical management. Current studies have reported large HCC detections using automatic algorithms, but there is a lack of research on automatic detection of small HCCs (sHCCs). This st...
Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks.
BMC medical imaging
Apr 13, 2021
BACKGROUND: In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for auto...
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