Automated Reconstruction Of The Cardiac Chambers From MRI
· Allows for the fast and accurate measurements of ventricle volume, pressure, mass, and wall thickness. · Can detect motion abnormalities · Fully automated and comparable to manually modeled results
Cardiac imaging
Manual delineation, the current standard for modeling the left ventricle (LV) is tedious, time consuming, and prone to inaccuracy. Researchers have struggled in developing automated segmentation of the LV. These struggles arise from various problems including difficulties measuring blood flow differences and dynamic heart motion, differentiating muscle types, and dealing with MRI noise. Thus, there is a need for a faster, more accurate automated modeling method. Researchers at UCI have addressed this need by developing a fully automated, accurate, and robust modeling method. This method uses new machine learning algorithms to learn from collected raw data. First, convolutional neural networks are used to automatically detect the heart’s chamber from MRI images. Then, autoencoders infer the shape of the LV. These shapes are then incorporated into deformable models to improve the accuracy, robustness, and computational time of the modeling process.
20170109881
State Of Development Reduced to practice Tech ID/UC Case 28744/2015-934-0 Related Cases 2015-934-0
USA
