This technology is a deep learning approach for estimating tissue displacement in the heart and arteries from ultrasound images that can be utilized to improve the performance of elasticity imaging techniques.
Current techniques for measuring tissue displacement are unable to handle the complex motion of tissue and lack quantitative analysis. Tissue displacement measurements are therefore under-utilized in clinical applications such as diagnosing and monitoring cardiovascular diseases and certain forms of cancer.
This technology is a deep learning model called Voxelmorph that is trained on ultrasound displacement images of the heart and arteries to improve the performance of elasticity imaging techniques. The model learns the physiological displacement patterns of cardiovascular tissues to improve pulse wave imaging and myocardial elastography performance. Ultrasound images used for training are acquired using commercially available instruments and analyzed in MATLAB. This approach is compatible with commercial ultrasound machines that use elasticity imaging modalities, enabling real time analysis of ultrasound images to generate tissue displacement maps.
This technology has been validated using human common carotid arteries in vivo.
IR CU21356
Licensing Contact: Dovina Qu
