Abstract #1030

SUBSTITUTING GADOLINIUM IN BRAIN MRI USING DEEPCONTRAST: A PROOF-OF-CONCEPT STUDY IN MICE

Haoran Sun¹, Xueqing Liu¹, Xinyang Feng¹, Chen Liu², Nanyan Zhu³, Sabrina Josefina Gjerswold-Selleck¹, Hong-Jian Wei^4,5, Pavan Shankar Upadhyayula^5,6, Angeliki Mela^5,6, Cheng-Chia Wu^4,5, Peter Canoll^5,6, Andrew F. Laine¹, John Thomas Vaughan¹, Scott A. Small^7,8,9, and Jia Guo^7,10

¹Department of Biomedical Engineering, Columbia University, New York, NY, United States, ²Department of Electrical Engineering, Columbia University, New York, NY, United States, ³Department of Biological Science, Columbia University, New York, NY, United States, ⁴Department of Radiation Oncology, Columbia University, New York, NY, United States, ⁵Columbia University Irving Medical Center, Columbia University, New York, NY, United States, ⁶Department of Pathology and Cell Biology, Columbia University, New York, NY, United States, ⁷Department of Psychiatry, Columbia University, New York, NY, United States, ⁸Departments of Neurology, Columbia University, New York, NY, United States, ⁹Departments of Radiology, Columbia University, New York, NY, United States, ¹⁰Mortimer B. Zickerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States

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. Recent studies suggest that the exogenous gadolinium based contrast agent (GBCA) can accumulate in the brain after frequent use. Here, we develop and optimize a deep learning algorithm, DeepContrast, which 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.

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