AIMC Topic: Artifacts

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Enhancing repeatability of follicle counting with deep learning reconstruction high-resolution MRI in PCOS patients.

Scientific reports Jan 7, 2025
Follicle count, a pivotal metric in the adjunct diagnosis of polycystic ovary syndrome (PCOS), is often underestimated when assessed via transvaginal ultrasonography compared to MRI. Nevertheless, the repeatability of follicle counting using traditio...
Female Reproducibility of Results Young Adult Humans Prospective Studies Magnetic Resonance Imaging Polycystic Ovary Syndrome Artifacts Adult Deep Learning Image Processing, Computer-Assisted Ovarian Follicle
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DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN-Transformer Network.

Sensors (Basel, Switzerland) Jan 3, 2025
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by variou...
Brain Deep Learning Electroencephalography Signal Processing, Computer-Assisted Artifacts Signal-To-Noise Ratio Humans Neural Networks, Computer Algorithms
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SISMIK for Brain MRI: Deep-Learning-Based Motion Estimation and Model-Based Motion Correction in k-Space.

IEEE transactions on medical imaging Jan 2, 2025
MRI, a widespread non-invasive medical imaging modality, is highly sensitive to patient motion. Despite many attempts over the years, motion correction remains a difficult problem and there is no general method applicable to all situations. We propos...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Motion Algorithms Artifacts Brain Movement Humans Deep Learning
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MrSeNet: Electrocardiogram signal denoising based on multi-resolution residual attention network.

Journal of electrocardiology Dec 27, 2024
Electrocardiography (ECG) is a widely used, non-invasive, and cost-effective diagnostic method that plays a crucial role in the early detection and management of cardiac conditions. However, the ECG signal is easily disrupted by various noise signals...
Humans Algorithms Databases, Factual Signal Processing, Computer-Assisted Deep Learning Artifacts Electrocardiography Signal-To-Noise Ratio
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Prior-FOVNet: A Multimodal Deep Learning Framework for Megavoltage Computed Tomography Truncation Artifact Correction and Field-of-View Extension.

Sensors (Basel, Switzerland) Dec 25, 2024
Megavoltage computed tomography (MVCT) plays a crucial role in patient positioning and dose reconstruction during tomotherapy. However, due to the limited scan field of view (sFOV), the entire cross-section of certain patients may not be fully covere...
Image Processing, Computer-Assisted Artifacts Phantoms, Imaging Algorithms Tomography, X-Ray Computed Deep Learning Humans
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Fast motion-compensated reconstruction for 4D-CBCT using deep learning-based groupwise registration.

Biomedical physics & engineering express Dec 23, 2024
. Previous work has that deep learning (DL)-enhanced 4D cone beam computed tomography (4D-CBCT) images improve motion modeling and subsequent motion-compensated (MoCo) reconstruction for 4D-CBCT. However, building the motion model at treatment time v...
Movement Four-Dimensional Computed Tomography Humans Algorithms Deep Learning Cone-Beam Computed Tomography Image Processing, Computer-Assisted Artifacts
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Feasibility/clinical utility of half-Fourier single-shot turbo spin echo imaging combined with deep learning reconstruction in gynecologic magnetic resonance imaging.

Abdominal radiology (New York) Dec 18, 2024
BACKGROUND: When antispasmodics are unavailable, the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER; called BLADE by Siemens Healthineers) or half Fourier single-shot turbo spin echo (HASTE) is clinically used...
Aged Adult Middle Aged Retrospective Studies Female Image Interpretation, Computer-Assisted Artifacts Fourier Analysis Humans Deep Learning Magnetic Resonance Imaging Feasibility Studies
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Deep learning methods for 3D magnetic resonance image denoising, bias field and motion artifact correction: a comprehensive review.

Physics in medicine and biology Nov 28, 2024
Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufa...
Movement Humans Artifacts Deep Learning Imaging, Three-Dimensional Signal-To-Noise Ratio Image Processing, Computer-Assisted Magnetic Resonance Imaging
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An integrated approach to a predictive and ranking model of use error using fuzzy BWM and fuzzy TOPSIS.

International journal of occupational safety and ergonomics : JOSE Nov 23, 2024
Avoiding error in handling artifacts is crucial for achieving a high level of system reliability and safety assessment. This study develops a predictive and ranking model of use error (PRUE). In the first phase, use errors are systematically detected...
Artifacts Models, Theoretical Humans Fuzzy Logic Reproducibility of Results Ventilators, Mechanical
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GraFMRI: A graph-based fusion framework for robust multi-modal MRI reconstruction.

Magnetic resonance imaging Nov 17, 2024
PURPOSE: This study introduces GraFMRI, a novel framework designed to address the challenges of reconstructing high-quality MRI images from undersampled k-space data. Traditional methods often suffer from noise amplification and loss of structural de...
Signal-To-Noise Ratio Humans Neural Networks, Computer Magnetic Resonance Imaging Multimodal Imaging Algorithms Artifacts Brain Image Processing, Computer-Assisted
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