AIMC Topic: Contrast Media
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Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT.
The British journal of radiology
May 11, 2020
OBJECTIVE: Establish a workflow that utilizes convolutional neural nets (CNN) to classify solid, lipid-poor, contrast enhancing renal masses using multiphase contrast enhanced CT (CECT) images and to assess the performance of the resulting network.
Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.
Computational and mathematical methods in medicine
May 5, 2020
Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper,...
Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
European radiology
Apr 29, 2020
OBJECTIVES: To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images.
High spatiotemporal resolution dynamic contrast-enhanced MRI improves the image-based discrimination of histopathology risk groups of peripheral zone prostate cancer: a supervised machine learning approach.
European radiology
Apr 23, 2020
OBJECTIVE: To assess if adding perfusion information from dynamic contrast-enhanced (DCE MRI) acquisition schemes with high spatiotemporal resolution to T2w/DWI sequences as input features for a gradient boosting machine (GBM) machine learning (ML) c...
Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Apr 15, 2020
Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures down to tens of micrometers. The image processing methods for the spatiotemporal filtering needed in...
Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience.
AJR. American journal of roentgenology
Apr 14, 2020
The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. Retrospective review (April-May 2019) of the cases of adults...
Nondestructive Detection of Targeted Microbubbles Using Dual-Mode Data and Deep Learning for Real-Time Ultrasound Molecular Imaging.
IEEE transactions on medical imaging
Apr 9, 2020
Ultrasound molecular imaging (UMI) is enabled by targeted microbubbles (MBs), which are highly reflective ultrasound contrast agents that bind to specific biomarkers. Distinguishing between adherent MBs and background signals can be challenging in vi...
Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach.
European radiology experimental
Apr 6, 2020
BACKGROUND: To evaluate whether machine learning algorithms allow the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT).
Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).
Physics in medicine and biology
Mar 19, 2020
Develop a machine learning-based method to generate multi-contrast anatomical textures in the 4D extended cardiac-torso (XCAT) phantom for more realistic imaging simulations. As a pilot study, we synthesize CT and CBCT textures in the chest region. F...
Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy.
Circulation. Arrhythmia and electrophysiology
Mar 18, 2020
BACKGROUND: Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-...
Cardiomyopathies
Fourier Analysis
Gadolinium DTPA
Death, Sudden, Cardiac
Aged
Registries
Male
Arrhythmias, Cardiac
Ventricular Function, Left
United States
Heart Rate
Prognosis
Stroke Volume
Humans
Middle Aged
Contrast Media
Myocardial Ischemia
Retrospective Studies
Risk Assessment
Magnetic Resonance Imaging
Machine Learning
Female
Diagnosis, Computer-Assisted
Action Potentials
Imaging, Three-Dimensional
Risk Factors
Predictive Value of Tests
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