2 citations · 3 across the 4 of their papers we have counts for
4 papers
INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT
Idan Tankel, Nir Mazor, Rafi Brada +2
Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported following established guidelines. Traditional manual inspect…
Deep learning-based reconstruction of highly accelerated 3D MRI
Sangtae Ahn, Uri Wollner, Graeme McKinnon +10
Purpose: To accelerate brain 3D MRI scans by using a deep learning method for reconstructing images from highly-undersampled multi-coil k-space data Methods: DL-Speed, an unrolled…
High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss
Ke Wang, Jonathan I Tamir, Alfredo De Goyeneche +4
Purpose: To improve reconstruction fidelity of fine structures and textures in deep learning (DL) based reconstructions. Methods: A novel patch-based Unsupervised Feature Loss (UFL…
A Novel Approach for Correcting Multiple Discrete Rigid In-Plane Motions Artefacts in MRI Scans
Michael Rotman, Rafi Brada, Israel Beniaminy +3
Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan. While many method…