This technology is a deep learning framework that generates contrast-enhanced MRI information from pre-contrast scans, enabling safer lesion imaging with reduced reliance on gadolinium-based contrast agents.
The current standard for enhancing MRI lesion visualization is the use of gadolinium-based contrast agents, but these agents require injection and raise concerns about gadolinium retention, potential toxicity, and added procedural complexity. Despite these limitations, contrast enhancement remains important for accurate lesion detection and assessment. As a result, there is a need for noninvasive approaches that preserve diagnostic contrast without the need to administer contrast agents.
This technology, called DeepContrast, is a deep learning-based imaging platform that predicts contrast-enhanced MRI information from a single pre-contrast T1-weighted MRI scan. The model is trained on paired non-contrast and contrast-enhanced MRI images to learn how enhancement patterns relate to baseline image features. It then generates voxel-level enhancement maps that preserve structural lesion information and support lesion visualization and assessment. By predicting enhancement without administered gadolinium in certain use cases, the technology may enable less invasive MRI workflows.
IR CU26243
Licensing Contact: Jerry Kokoshka
