AIMC Topic: Contrast Media
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Impact of Deep Learning-based Optimization Algorithm on Image Quality of Low-dose Coronary CT Angiography with Noise Reduction: A Prospective Study.
Academic radiology
Dec 19, 2019
RATIONALE AND OBJECTIVES: To evaluate deep learning (DL)-based optimization algorithm for low-dose coronary CT angiography (CCTA) image noise reduction and image quality (IQ) improvement.
Editorial on "Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue".
Journal of magnetic resonance imaging : JMRI
Dec 17, 2019
Deep learning-based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual-energy CT-based approach.
Medical physics
Dec 3, 2019
PURPOSE: The aim of this study was to develop a deep learning (DL) method for generating virtual noncontrast (VNC) computed tomography (CT) images from contrast-enhanced (CE) CT images (VNC ) and to evaluate its performance in dose calculations for h...
Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics.
IEEE journal of biomedical and health informatics
Nov 28, 2019
OBJECTIVE: Histologic grade and Ki-67 proliferation status are important clinical indictors for breast cancer prognosis and treatment. The purpose of this study is to improve prediction accuracy of these clinical indicators based on tumor radiomic an...
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors.
Artificial intelligence in medicine
Nov 27, 2019
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative infor...
A fuzzy clustering based color-coded diagram for effective illustration of blood perfusion parameters in contrast-enhanced ultrasound videos.
Computer methods and programs in biomedicine
Nov 19, 2019
BACKGROUND AND OBJECTIVE: Early identification and diagnosis of tumors are of great significance to improve the survival rate of patients. Amongst other techniques, contrast-enhanced ultrasound is an important means to help doctors diagnose tumors. D...
Atrial scar quantification via multi-scale CNN in the graph-cuts framework.
Medical image analysis
Nov 16, 2019
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to...
Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.
EBioMedicine
Nov 15, 2019
BACKGROUND: Current guidelines recommend surgical resection as the first-line option for patients with solitary hepatocellular carcinoma (HCC); unfortunately, postoperative recurrence rate remains high and there is no reliable prediction tool. We exp...
Neoplasm Recurrence, Local
Hepatectomy
Proportional Hazards Models
Radiographic Image Enhancement
Image Processing, Computer-Assisted
Contrast Media
Workflow
Algorithms
Prognosis
Female
Humans
Retrospective Studies
Male
Machine Learning
Tomography, X-Ray Computed
Liver Neoplasms
Carcinoma, Hepatocellular
Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI.
Diagnostic and interventional imaging
Nov 11, 2019
OBJECTIVE: To assess the diagnostic value of machine learning-based texture feature analysis of late gadolinium enhancement images on cardiac magnetic resonance imaging (MRI) for assessing the presence of ventricular tachyarrhythmia (VT) in patients ...
Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.
Journal of magnetic resonance imaging : JMRI
Nov 1, 2019
BACKGROUND: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported.
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