AIMC Topic: Artifacts

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Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy.

Medical image analysis
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies...

Unsupervised anomaly detection using generative adversarial networks in H-MRS of the brain.

Journal of magnetic resonance (San Diego, Calif. : 1997)
The applicability of generative adversarial networks (GANs) capable of unsupervised anomaly detection (AnoGAN) was investigated in the management of quality of H-MRS human brain spectra at 3.0 T. The AnoGAN was trained in an unsupervised manner solel...

Deep learning-based metal artefact reduction in PET/CT imaging.

European radiology
OBJECTIVES: The susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate atte...

Magnetic resonance parameter mapping using model-guided self-supervised deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping.

Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions.

NeuroImage
Head motion during MRI acquisition presents significant challenges for neuroimaging analyses. In this work, we present a retrospective motion correction framework built on a Fourier domain motion simulation model combined with established 3D convolut...

Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography.

Journal of endodontics
INTRODUCTION: Tooth segmentation on cone-beam computed tomographic (CBCT) imaging is a labor-intensive task considering the limited contrast resolution and potential disturbance by various artifacts. Fully automated tooth segmentation cannot be achie...

High quality and fast compressed sensing MRI reconstruction via edge-enhanced dual discriminator generative adversarial network.

Magnetic resonance imaging
Generative adversarial networks (GAN) are widely used for fast compressed sensing magnetic resonance imaging (CSMRI) reconstruction. However, most existing methods are difficult to make an effective trade-off between abstract global high-level featur...

Ultrashort echo time time-spatial labeling inversion pulse magnetic resonance angiography with denoising deep learning reconstruction for the assessment of abdominal visceral arteries.

Journal of magnetic resonance imaging : JMRI
Current contrast-enhanced magnetic resonance angiography (MRA) and non-contrast-enhanced balanced steady-state free precession (bSSFP) MRA cause susceptibility artifacts from metallic devices in assessing endovascular visceral-artery interventions. T...

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ...