AIMC Topic: Artifacts

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Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

Medical physics
PURPOSE: This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images.

Towards frameless maskless SRS through real-time 6DoF robotic motion compensation.

Physics in medicine and biology
Stereotactic radiosurgery (SRS) uses precise dose placement to treat conditions of the CNS. Frame-based SRS uses a metal head ring fixed to the patient's skull to provide high treatment accuracy, but patient comfort and clinical workflow may suffer. ...

Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.

NeuroImage. Clinical
Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group...

Please Don't Move-Evaluating Motion Artifact From Peripheral Quantitative Computed Tomography Scans Using Textural Features.

Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry
Most imaging methods, including peripheral quantitative computed tomography (pQCT), are susceptible to motion artifacts particularly in fidgety pediatric populations. Methods currently used to address motion artifact include manual screening (visual ...

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

IEEE journal of biomedical and health informatics
Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applicati...

A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems.

Methods (San Diego, Calif.)
EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation...

Scanning double-sided documents without incurring show-through by learning to fuse two complementary images using multilayer perceptron.

PloS one
This paper presents a novel method for scanning duplex-printed documents without incurring the unwanted show-through artifact. The proposed method achieves the goal of eliminating the leaked-out reverse-side content by fusing a white backed scan imag...

Deep image mining for diabetic retinopathy screening.

Medical image analysis
Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert...

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

Journal of neural engineering
OBJECTIVE: Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a...