AIMC Topic: Contrast Media
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Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors.
Radiology
Mar 17, 2020
Background Brain metastases are manually identified during stereotactic radiosurgery (SRS) treatment planning, which is time consuming and potentially challenging. Purpose To develop and investigate deep learning (DL) methods for detecting brain meta...
GAN and dual-input two-compartment model-based training of a neural network for robust quantification of contrast uptake rate in gadoxetic acid-enhanced MRI.
Medical physics
Feb 19, 2020
PURPOSE: Gadoxetic acid uptake rate (k ) obtained from dynamic, contrast-enhanced (DCE) magnetic resonance imaging (MRI) is a promising measure of regional liver function. Clinical exams are typically poorly temporally characterized, as seen in a low...
A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI.
European radiology experimental
Jan 28, 2020
BACKGROUND: Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature.
Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.
AJR. American journal of roentgenology
Jan 22, 2020
The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. Our prospective mult...
The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas.
Clinical radiology
Jan 21, 2020
AIM: To explore the value of quantitative texture analysis of conventional magnetic resonance imaging (MRI) sequences using artificial neural networks (ANN) for the differentiation of high-grade gliomas (HGG) and low-grade gliomas (LGG).
Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time.
European journal of radiology
Jan 14, 2020
PURPOSE: To develop and evaluate the performance of a fully-automated convolutional neural network (CNN)-based algorithm to evaluate hepatobiliary phase (HBP) adequacy of gadoxetate disodium (EOB)-enhanced MRI. Secondarily, we explored the potential ...
Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning.
Ultrasound in medicine & biology
Jan 8, 2020
Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell....
Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning.
AJR. American journal of roentgenology
Jan 8, 2020
This study evaluated the utility of a deep learning method for determining whether a small (≤ 4 cm) solid renal mass was benign or malignant on multiphase contrast-enhanced CT. This retrospective study included 1807 image sets from 168 pathological...
A Transfer-Learning Approach for Accelerated MRI Using Deep Neural Networks.
Magnetic resonance in medicine
Jan 3, 2020
PURPOSE: Neural networks have received recent interest for reconstruction of undersampled MR acquisitions. Ideally, network performance should be optimized by drawing the training and testing data from the same domain. In practice, however, large dat...
Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.
European radiology
Jan 3, 2020
OBJECTIVES: We aimed to establish and validate an artificial intelligence-based radiomics strategy for predicting personalized responses of hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) session by quantitatively analy...
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