Technologies
This technology is a deep learning model that uses a U-Net neural network to generate nonfat-suppressed breast MRI images from standard fat-suppressed scans, allowing for diagnostic evaluation.
T1-weighted fat-suppressed imaging is the current standard for detecting lesions and guiding diagnosis for breast cancer patients. Non-fat-suppressed images are often not acquired in scanning protocols, even though they can be essential in identifying fat necrosis, fatty lesions, and background parenchymal enhancement, all of which are key biomarkers of breast cancer risk. The lack of both fat- and nonfat-suppressed images in standard MRI protocols limits treatment, necessitating new computational methods to improve diagnostic accuracy and efficiency without increasing scan time or patient burden.
This technology offers a modular deep learning framework called Sat2Nu for generating nonfat-suppressed images from fat-suppressed MRI scans. It employs a U-Net architecture optimized to process both image types, allowing for accurate training even with limited datasets. Validation studies demonstrated low reconstruction error in the generated images compared to true nonfat-suppressed images, supporting its potential for improving MRI workflows without requiring additional scan acquisitions, particularly in breast imaging applications.
IR CU26094
Licensing Contact: Joan Martinez