AIMC Topic: Artifacts

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StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images.

Physical and engineering sciences in medicine
The highly contagious nature of Coronavirus disease 2019 (Covid-19) resulted in a global pandemic. Due to the relatively slow and taxing nature of conventional testing for Covid-19, a faster method needs to be in place. The current researches have su...

Sparse-view CT reconstruction based on multi-level wavelet convolution neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Sparse-view computed tomography (CT) is a recent approach to reducing the radiation dose in patients and speeding up the data acquisition. Consequently, sparse-view CT has been of particular interest among researchers within the CT community. Advance...

Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning.

NMR in biomedicine
The aim of this study was to develop a deep neural network for respiratory motion compensation in free-breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteer...

Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation.

IEEE transactions on medical imaging
Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependent on the quality of the image that is being segmented. A commonly negl...

Mitigation of ocular artifacts for EEG signal using improved earth worm optimization-based neural network and lifting wavelet transform.

Computer methods in biomechanics and biomedical engineering
An Electroencephalogram (EEG) is often tarnished by various categories of artifacts. Numerous efforts have been taken to improve its quality by eliminating the artifacts. The EEG involves the biological artifacts (ocular artifacts, ECG and EMG artifa...

A Deep Learning Approach to Resolve Aliasing Artifacts in Ultrasound Color Flow Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Despite being used clinically as a noninvasive flow visualization tool, color flow imaging (CFI) is known to be prone to aliasing artifacts that arise due to fast blood flow beyond the detectable limit. From a visualization standpoint, these aliasing...

Deep convolution neural networks based artifact suppression in under-sampled radial acquisitions of myocardial T mapping images.

Physics in medicine and biology
We developed a deep convolutional neural network (CNN) based method to remove streaking artefact from accelerated radial acquisitions of myocardial T -mapping images. A deep CNN based on a modified U-Net architecture was developed and trained to remo...

Dynamic MRI reconstruction with end-to-end motion-guided network.

Medical image analysis
Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of body regions. Modeling such information into the MRI reconstruction process produces temporally coh...

A deep learning framework for quality assessment and restoration in video endoscopy.

Medical image analysis
Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, specular reflections, floating objects and pixel saturation impede the visual interpretation and the a...

Intravenous Line Phase-Wrap Artifact at Bilateral Axial 3-T Breast MRI: Identification, Analysis, and Solution.

Radiology. Imaging cancer
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.