{"id":"CU21356","slug":"deep-learning-model-for--CU21356","source":{"id":"CU21356","dataset":"techtransfer","title":"Deep learning model for analyzing tissue displacement from ultrasound imaging","description_":"<p>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.  </p>\r\r<h2>Unmet Need: Robust quantitative analysis of tissue displacement</h2>\r\r<p>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. </p>\r\r<h2>The Technology: Commercially compatible deep learning technique to quantify tissue displacement in real time</h2>\r\r<p>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.</p>\r\r<p>This technology has been validated using human common carotid arteries in vivo. </p>\r\r<h2>Applications:</h2>\r\r<ul>\r<li>Diagnostic imaging for cancer and cardio-vascular diseases</li>\r<li>Clinical tool for real-time measurements of tissue displacement </li>\r<li>Clinical tool for improved performance of Pulse Wave Imaging and Myocardial Elastography</li>\r<li>Research tool for characterizing cardiovascular diseases</li>\r</ul>\r\r<h2>Advantages:</h2>\r\r<ul>\r<li>Compatible with commercial ultrasound systems</li>\r<li>Captures complex tissue motion patterns</li>\r<li>Provides robust and quantitative tissue displacement measurements </li>\r<li>Enables real-time tissue displacement analysis</li>\r</ul>\r\r<h2>Lead Inventor:</h2>\r\r<p><a href=\"https://www.bme.columbia.edu/faculty-staff/directory/elisa-e-konofagou\">Elisa Konofagou, Ph.D.</a></p>\r\r<h2>Related Publications:</h2>\r\r<ul>\r<li><a href=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC10528442/\">Karageorgos GM, Liang P, Mobadersany N, Gami P, Konofagou EE. “Unsupervised deep learning-based displacement estimation for vascular elasticity imaging applications” Phys Med Biol. 2023 Jul 24; 68(15).</a></li>\r</ul>\r\r<h2>Tech Ventures Reference:</h2>\r\r<ul>\r<li><p>IR CU21356</p></li>\r<li><p>Licensing Contact: <a href=\"mailto:techtransfer@columbia.edu\">Dovina Qu</a></p></li>\r</ul>","tags":["Artery","Cancer","Cardiac muscle","Common carotid artery","Deep learning","Elastography","MATLAB","Medical imaging","Unsupervised learning"],"file_number":"CU21356","collections":[],"meta_description":"Real-time deep learning maps ultrasound tissue displacement to improve elasticity imaging for cardiovascular and cancer diagnostics.","apriori_judge_output":"{\"scores\":{\"novelty\":4.0,\"potential_impact\":4.0,\"readiness\":3.0,\"scalability\":3.0,\"timeliness\":4.0},\"weighted_score\":3.75,\"risks\":[\"Potential data/data access constraints for broader validation.\",\"Competition from existing elastography/elasticity imaging methods.\",\"Regulatory considerations for clinical deployment in ultrasound systems.\"],\"one_sentence_take\":\"Strong novelty and potential impact with solid readiness; address validation breadth and regulatory pathway to maximize adoption.\"}","inventors":["Elisa Konofagou","Grigorios Marios Karageorgos"],"manager":"Dovina Qu","depts":["Biomedical Engineering"],"divs":["Fu Foundation School of Engineering and Applied Science (SEAS)"],"date_released":"2026-06-24"},"highlight":{},"matched_queries":null,"score":0.0}