AIMC Topic: Artifacts

Clear Filters Showing 511 to 520 of 721 articles

A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts.

International journal of computer assisted radiology and surgery
PURPOSE: The main purpose of this work is to develop, apply, and evaluate an efficient approach for breast density estimation in magnetic resonance imaging data, which contain strong artifacts including intensity inhomogeneities.

SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network.

Zeitschrift fur medizinische Physik
Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson's disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonanc...

Simulation-based deep artifact correction with Convolutional Neural Networks for limited angle artifacts.

Zeitschrift fur medizinische Physik
Non-conventional scan trajectories for interventional three-dimensional imaging promise low-dose interventions and a better radiation protection to the personnel. Circular tomosynthesis (cTS) scan trajectories yield an anisotropical image quality dis...

Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks.

Medical physics
PURPOSE: In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in th...

Quantifying lung ultrasound comets with a convolutional neural network: Initial clinical results.

Computers in biology and medicine
Lung ultrasound comets are "comet-tail" artifacts appearing in lung ultrasound images. They are particularly useful in detecting several lung pathologies and may indicate the amount of extravascular lung water. However, the comets are not always well...

Robust Single-Shot T Mapping via Multiple Overlapping-Echo Acquisition and Deep Neural Network.

IEEE transactions on medical imaging
Quantitative magnetic resonance imaging (MRI) is of great value to both clinical diagnosis and scientific research. However, most MRI experiments remain qualitative, especially dynamic MRI, because repeated sampling with variable weighting parameter ...

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

Magnetic resonance in medicine
PURPOSE: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectr...

An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms.

Physiological measurement
OBJECTIVE: Intracranial pressure (ICP) is an important and established clinical measurement that is used in the management of severe acute brain injury. ICP waveforms are usually triphasic and are susceptible to artifact because of transient catheter...

Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Synthetic FLAIR images are of lower quality than conventional FLAIR images. Here, we aimed to improve the synthetic FLAIR image quality using deep learning with pixel-by-pixel translation through conditional generative adversa...

A Deep Learning-Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: MR imaging rescans and recalls can create large hospital revenue loss. The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series.