AIMC Topic: Contrast Media
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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.
Improvement of late gadolinium enhancement image quality using a deep learning-based reconstruction algorithm and its influence on myocardial scar quantification.
European radiology
Nov 21, 2020
OBJECTIVES: The aim of this study was to assess the effect of a deep learning (DL)-based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification.
Intravenous Line Phase-Wrap Artifact at Bilateral Axial 3-T Breast MRI: Identification, Analysis, and Solution.
Radiology. Imaging cancer
Nov 6, 2020
PURPOSE: To understand and remove the source of a phase-wrap artifact produced by residual contrast agent in the intravenous line during acquisition of bilateral axial 3-T dynamic contrast material-enhanced (DCE) breast MRI.
Automatic Detection and Scoring of Kidney Stones on Noncontrast CT Images Using S.T.O.N.E. Nephrolithometry: Combined Deep Learning and Thresholding Methods.
Molecular imaging and biology
Oct 27, 2020
PURPOSE: To develop and validate a deep learning and thresholding-based model for automatic kidney stone detection and scoring according to S.T.O.N.E. nephrolithometry.
Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors.
Korean journal of radiology
Oct 21, 2020
OBJECTIVE: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on d...
Artificial Intelligence Applied to Breast MRI for Improved Diagnosis.
Radiology
Oct 20, 2020
Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient tr...
Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging.
NeuroImage
Oct 16, 2020
We introduce the first-ever statistical framework for estimating the age of Multiple Sclerosis (MS) lesions from magnetic resonance imaging (MRI). Estimating lesion age is an important step when studying the longitudinal behavior of MS lesions and ca...
Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique.
European radiology
Oct 8, 2020
OBJECTIVES: To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychrom...
Detecting Large Vessel Occlusion at Multiphase CT Angiography by Using a Deep Convolutional Neural Network.
Radiology
Sep 29, 2020
Background Large vessel occlusion (LVO) stroke is one of the most time-sensitive diagnoses in medicine and requires emergent endovascular therapy to reduce morbidity and mortality. Leveraging recent advances in deep learning may facilitate rapid dete...
Improving Quantitative Magnetic Resonance Imaging Using Deep Learning.
Seminars in musculoskeletal radiology
Sep 29, 2020
Deep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parame...
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