MRI

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

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

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

Automated Vessel Segmentation for 2D Phase Contrast MR Using Deep Learning

Fully automatic extraction of mitral valve annulus motion parameters on long axis CINE CMR using deep learning

Automatic derived parameter for valve motion assessment and interactive visualization dashboard for cluster analysis and anomaly detec.

Assessment of cardiac valve motion on time-resolved MRI images using deep learning

M.Sc. Thesis carried out in collaboration with Siemens Healthineers. Research state-of-the-art literature on cardiac MRI slice tracking and medical landmark detection. Develop a robust novel valve tracking 3D-CNN algorithm based on heatmap regression

Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images

The project aims to combine multi-sequence CMR data to classify the myocardial pathology. It willl be segmented into into normal, infarcted and edema regions, which is essential in the diagnosis and treatment management for patients suffering from myocardial infarction (MI)