BACKGROUND: In ultrasound imaging using plane-wave (PW) techniques, image quality and contrast often suffer, especially when examining anechoic structures. Traditional beamforming methods like Delay-and-Sum or coherent PW compounding face limitations...
This narrative review presents deep learning-based strategies for generating synthetic 3D CT-like images from biplanar or multiplanar 2D X-ray data. Current limitations of conventional CT imaging are discussed, hence emphasizing the potential of synt...
IEEE transactions on bio-medical engineering
Aug 1, 2025
Semi-supervised learning (SSL) enables the accurate segmentation of medical images with limited available labeled data. However, its performance usually lags fully supervised methods that require the whole dataset to be labeled. We propose a novel SS...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Aug 1, 2025
Recent innovations in vector flow imaging promise to bring the modality closer to clinical application and allow for more comprehensive, high frame-rate vascular assessments. One such innovation is plane-wave multi-angle vector Doppler, where pulsed ...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Aug 1, 2025
The reflection ultrasound computed tomography (UCT) is gaining prominence as an essential instrument for breast cancer screening. However, reflection UCT quality is often compromised by the variability in sound speed across breast tissue. Traditional...
Scan-specific deep learning strategies have been proposed for parallel imaging reconstruction in which auto-calibrated signals (ACS) are used for training. Here, we introduce methods to objectively optimize architecture and training details. In addit...
Convolutional Neural Networks (CNNs) have achieved remarkable success in breast ultrasound image segmentation, but they still face several challenges when dealing with breast lesions. Due to the limitations of CNNs in modeling long-range dependencies...
Computed tomography (CT) systems are indispensable for diagnostics but pose risks due to radiation exposure. Low-dose CT (LDCT) mitigates these risks but introduces noise and artifacts that compromise diagnostic accuracy. While deep learning methods,...
Conventional unimodal computer vision models, trained on limited bespoke waste datasets, face significant challenges in classifying waste images in material recovery facilities, where waste appears in diverse forms. Maintaining performance of these m...
Three-dimensional visualization is increasingly used in dentistry for diagnostics, education, and treatment design. The accurate replication of geometry and color is crucial for these applications. Image-based rendering, which uses 2-dimensional phot...
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