  Cerebral blood volume (CBV) is a hemodynamic correlate of oxygen metabolism
and reflects brain activity and function. High-resolution CBV maps can be
generated using the steady-state gadolinium-enhanced MRI technique. Such a
technique requires an intravenous injection of exogenous gadolinium based
contrast agent (GBCA) and recent studies suggest that the GBCA can accumulate
in the brain after frequent use. We hypothesize that endogenous sources of
contrast might exist within the most conventional and commonly acquired
structural MRI, potentially obviating the need for exogenous contrast. Here, we
test this hypothesis by developing and optimizing a deep learning algorithm,
which we call DeepContrast, in mice. We find that DeepContrast performs equally
well as exogenous GBCA in mapping CBV of the normal brain tissue and enhancing
glioblastoma. Together, these studies validate our hypothesis that a deep
learning approach can potentially replace the need for GBCAs in brain MRI.

Quantitative Methods;Computer Vision and Pattern Recognition;Image and Video Processing