AIMC Topic: Artifacts

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Sparse Representation Over Learned Dictionaries on the Riemannian Manifold for Automated Grading of Nuclear Pleomorphism in Breast Cancer.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Breast cancer is found to be the most pervasive type of cancer among women. Computer aided detection and diagnosis of cancer at the initial stages can increase the chances of recovery and thus reduce the mortality rate through timely prognosis and ad...

Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.

Magnetic resonance in medicine
PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability...

EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis.

Computational and mathematical methods in medicine
Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of trad...

Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning.

IEEE transactions on medical imaging
The accurate segmentation of the brain surface in post-surgical computed tomography (CT) images is critical for image-guided neurosurgical procedures in epilepsy patients. Following surgical implantation of intracranial electrodes, surgeons require a...

Traditional machine learning for limited angle tomography.

International journal of computer assisted radiology and surgery
PURPOSE: The application of traditional machine learning techniques, in the form of regression models based on conventional, "hand-crafted" features, to artifact reduction in limited angle tomography is investigated.

Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction.

IEEE transactions on medical imaging
Accelerating the data acquisition of dynamic magnetic resonance imaging leads to a challenging ill-posed inverse problem, which has received great interest from both the signal processing and machine learning communities over the last decades. The ke...

Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images.

International journal of computer assisted radiology and surgery
PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) is a subcellular in vivo imaging technique capable of producing images that enable diagnosis of malign structural modifications in epithelial tissue. Images acquired with pCLE are, however, of...

Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning.

Journal of neuroscience methods
BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation.

Prospective motion correction improves the sensitivity of fMRI pattern decoding.

Human brain mapping
We evaluated the effectiveness of prospective motion correction (PMC) on a simple visual task when no deliberate subject motion was present. The PMC system utilizes an in-bore optical camera to track an external marker attached to the participant via...

A supervised learning approach for diffusion MRI quality control with minimal training data.

NeuroImage
Quality control (QC) is a fundamental component of any study. Diffusion MRI has unique challenges that make manual QC particularly difficult, including a greater number of artefacts than other MR modalities and a greater volume of data. The gold stan...