AIMC Topic: Artifacts
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VBVT-Net: VOI-Based VVBP-Tensor Network for High-Attenuation Artifact Suppression in Digital Breast Tomosynthesis Imaging.
IEEE transactions on medical imaging
Apr 3, 2025
High-attenuation (HA) artifacts may lead to obscured subtle lesions and lesion over-estimation in digital breast tomosynthesis (DBT) imaging. High-attenuation artifact suppression (HAAS) is vital for widespread DBT applications in clinic. The convent...
Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.
AJNR. American journal of neuroradiology
Apr 2, 2025
BACKGOUND AND PURPOSE: This study utilizes a physics-based approach to synthesize realistic MR artifacts and train a deep learning generative adversarial network (GAN) for use in artifact reduction on EPI, a crucial neuroimaging sequence with high ac...
A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 1, 2025
Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Fun...
Semi-supervised spatial-frequency transformer for metal artifact reduction in maxillofacial CT and evaluation with intraoral scan.
European journal of radiology
Mar 31, 2025
PURPOSE: To develop a semi-supervised domain adaptation technique for metal artifact reduction with a spatial-frequency transformer (SFTrans) model (Semi-SFTrans), and to quantitatively compare its performance with supervised models (Sup-SFTrans and ...
Artefacts of Change: The Disruptive Nature of Humanoid Robots Beyond Classificatory Concerns.
Science and engineering ethics
Mar 28, 2025
One characteristic of socially disruptive technologies is that they have the potential to cause uncertainty about the application conditions of a concept i.e., they are conceptually disruptive. Humanoid robots have done just this, as evidenced by dis...
Deep Guess acceleration for explainable image reconstruction in sparse-view CT.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 21, 2025
Sparse-view Computed Tomography (CT) is an emerging protocol designed to reduce X-ray dose radiation in medical imaging. Reconstructions based on the traditional Filtered Back Projection algorithm suffer from severe artifacts due to sparse data. In c...
DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT.
IEEE transactions on medical imaging
Mar 17, 2025
4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projection data will cause severe streak artifacts in 4D CBCT images. Existing deep learning (DL) methods ...
Metal Suppression Magnetic Resonance Imaging Techniques in Orthopaedic and Spine Surgery.
The Journal of the American Academy of Orthopaedic Surgeons
Mar 11, 2025
Implantation of metallic instrumentation is the mainstay of a variety of orthopaedic and spine surgeries. Postoperatively, imaging of the soft tissues around these implants is commonly required to assess for persistent, recurrent, and/or new patholog...
Self-supervised U-transformer network with mask reconstruction for metal artifact reduction.
Physics in medicine and biology
Mar 10, 2025
. Metal artifacts severely damaged human tissue information from the computed tomography (CT) image, posing significant challenges to disease diagnosis. Deep learning has been widely explored for the metal artifact reduction (MAR) task. Nevertheless,...
The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mar 7, 2025
The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical techniques. However, deep learning models can underperform if tra...
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