502 citations · 594 across the 16 of their papers we have counts for
7 papers · 1 filter
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
Marcel Beetz, Abhirup Banerjee, Vicente Grau
Global single-valued biomarkers of cardiac function typically used in clinical practice, such as ejection fraction, provide limited insight on the true 3D cardiac deformation proce…
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Marcel Beetz, Abhirup Banerjee, Julius Ossenberg-Engels +1
Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional…
Segment Anything Model for Medical Images?
Yuhao Huang, Xin Yang, Lian Liu +16
The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. Howev…
Multi-Modality Cardiac Image Computing: A Survey
Lei Li, Wangbin Ding, Liqun Huang +2
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and f…
A Deep learning Approach to Generate Contrast-Enhanced Computerised Tomography Angiography without the Use of Intravenous Contrast Agents
Anirudh Chandrashekar, Ashok Handa, Natesh Shivakumar +3
Contrast-enhanced computed tomography angiograms (CTAs) are widely used in cardiovascular imaging to obtain a non-invasive view of arterial structures. However, contrast agents are…
A Deep Learning Approach to Automate High-Resolution Blood Vessel Reconstruction on Computerized Tomography Images With or Without the Use of Contrast Agent
Anirudh Chandrashekar, Ashok Handa, Natesh Shivakumar +3
Existing methods to reconstruct vascular structures from a computed tomography (CT) angiogram rely on injection of intravenous contrast to enhance the radio-density within the vess…