Heart disease is highly prevalent in developed countries, causing 1 in 4 deaths. In this work we propose a method for a fully automated 4D reconstruction of the left ventricle of the heart. This can provide accurate information regarding the heart wall motion and in particular the hemodynamics of the ventricles. Such metrics are crucial for detecting heart function anomalies that can be an indication of heart disease. Our approach is fast, modular and extensible. In our testing, we found that generating the 4D reconstruction from a set of 250 MRI images takes less than a minute. The amount of time saved as a result of our work could greatly benefit physicians and cardiologist as they diagnose and treat patients.
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Automated Segmentation and 4-D reconstruction of the Heart Left Ventricle from CINE MRI
Heart disease is highly prevalent in developed countries,
causing 1 in 4 deaths. In this work we propose a method for
a fully automated 4D reconstruction of the left ventricle of the
heart. This can provide accurate information regarding the heart
wall motion and in particular the hemodynamics of the ventricles.
Such metrics are crucial for detecting heart function anomalies
that can be an indication of heart disease. Our approach is fast,
modular and extensible. In our testing, we found that generating
the 4D reconstruction from a set of 250 MRI images takes less
than a minute. The amount of time saved as a result of our work
could greatly benefit physicians and cardiologist as they diagnose
and treat patients.
Index Terms—Magnetic Resonance Imaging, segmentation,
reconstruction, cardiac, machine learning, ventricle
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- Award ID(s):
- 1646566
- NSF-PAR ID:
- 10130216
- Date Published:
- Journal Name:
- IEEE International Conference on Bioinformatics and Bioengineering (BIBE)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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