AIMC Topic: Artifacts
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Deep learning corrects artifacts in RASER MRI profiles.
Magnetic resonance imaging
Oct 24, 2024
A newly developed magnetic resonance imaging (MRI) approach is based on "Radiowave amplification by the stimulated emission of radiation" (RASER). RASER MRI potentially allows for higher resolution, is inherently background-free, and does not require...
Automated detection of motion artifacts in brain MR images using deep learning.
NMR in biomedicine
Oct 22, 2024
Quality assessment, including inspecting the images for artifacts, is a critical step during magnetic resonance imaging (MRI) data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep le...
Deep Neural Network-Based Empirical Mode Decomposition for Motor Imagery EEG Classification.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Oct 1, 2024
Motor imagery refers to the brain's response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals. However, EEG signals exhibit a low signal-to-noise ratio (SNR) due to various artifact...
Utilization of a natural language processing-based approach to determine the composition of artifact residues.
BMC bioinformatics
Sep 27, 2024
BACKGROUND: Determining the composition of artifact residues is a central problem in ancient residue metabolomics. This is done by comparing mass spectral features in common with an experimental artifact and an ancient artifact (standard method). Whi...
Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment.
Physiological measurement
Sep 16, 2024
We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. T...
A Learnable and Explainable Wavelet Neural Network for EEG Artifacts Detection and Classification.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 16, 2024
Electroencephalography (EEG) artifacts are very common in clinical diagnosis and can heavily impact diagnosis. Manual screening of artifact events is labor-intensive with little benefit. Therefore, exploring algorithms for automatic detection and cla...
Recurrent neural network-aided processing of incomplete free induction decays in H-MRS of the brain.
Journal of magnetic resonance (San Diego, Calif. : 1997)
Sep 12, 2024
In the case of limited sampling windows or truncation of free induction decays (FIDs) for artifact removal in proton magnetic resonance spectroscopy (H-MRS) and spectroscopic imaging (H-MRSI), metabolite quantification needs to be performed on incomp...
Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review.
European journal of radiology
Sep 7, 2024
BACKGROUND: Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnostic accuracy. With advancements in artificial intelligence, novel deep learning (DL)-based metal art...
Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.
Magma (New York, N.Y.)
Aug 30, 2024
OBJECTIVE: To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reco...
Imaging, Three-Dimensional
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Reproducibility of Results
White Matter
Algorithms
Young Adult
Signal-To-Noise Ratio
Aged
Brain
Adolescent
Adult
Image Interpretation, Computer-Assisted
Nervous System Diseases
Female
Male
Child, Preschool
Artifacts
Middle Aged
Child
Deep Learning
Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.
Magma (New York, N.Y.)
Aug 21, 2024
We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and...
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