Clinical Parameters

Fully automated AI-based cardiac motion parameter extraction – application to mitral and tricuspid valves on long-axis cine MR images
To automatically extract the valve-related motion parameters, the proposed AI-based system was developed and analysed on a large dataset. We investigated the robustness and feasibility of the system extensively on MV and TV related motion parameters. The system achieved human-level accuracy and can improve the workflow efficiency, automation and standardization of valve-related acquisitions or analyses.
Seung Su Yoon, Carola Fischer, Daniel Amsel, Maria Monzon, Solenn Toupin, Théo Pezel, Jérôme Garotç, Jens Wetzl, Andreas Maier, Daniel Giese
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Fully automated AI-based cardiac motion parameter extraction – application to mitral and tricuspid valves on long-axis cine MR images
Fully automatic extraction of mitral valve annulus motion parameters on long axis CINE CMR using deep learning
The analysis of mitral valve motion is known to be relevant in the diagnosis of cardiac dysfunction. Dynamic motion parameters can be …
Maria Monzon, Seung Su Yoon, Carola Fischer, Andreas Maier, Jens Wetz, Daniel Giese
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Fully automatic extraction of mitral valve annulus motion parameters on long axis CINE CMR using deep learning
Automated Vessel Segmentation for 2D Phase Contrast MR Using Deep Learning
Phase-contrast (PC) MRI is used to evaluate blood hemodynamics; however, it can be time consuming to process PC-MR data. In this work, …
Ning Jin, Maria Monzon, Teodora Chitiboi, Aaron Pruitt, Daniel Giese, Matthew Tong, Orlando P Simonetti
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Automated Vessel Segmentation for 2D Phase Contrast MR Using Deep Learning