This technology is a computational algorithm that can minimize the dose of gadolinium-based contrast agents used in magnetic resonance imaging (MRI) without compromising imaging quality.
Contrast agents, such as gadolinium (Gd), are commonly used during MRI for improving imaging signal. However, recent reports have suggested that some contrast agents may be retained in the body long-term and have associated toxicity. Furthermore, the cost of Gd agents is a large contributor to the high cost of MRI. As such, methods to reduce the dose of contrast agents required for optimal imaging can be cost effective and more beneficial for patients, potentially expanding the accessibility of MRI.
This technology describes a deep learning method which can reduce the amount of Gd-based contrast agents used for MRI. Test datasets of contrast-enhanced cerebral blood volume (CBV) mapping were collected from mouse brains and used for training and validation of the software. Application of this method was shown to reduce usage of the Gd contrast agent by at least five times without loss of signal in the image. As such, this method allows for optimization of contrast agent usage, potentially minimizing toxicity and costs associated with MRI.
This technology has been validated with contrast-enhanced cerebral blood volume mapping of mouse brains.
IR CU20041
Licensing Contact: Jerry Kokoshka
