Abstract #0060

Predicting Gadolinium Contrast Enhancement for Structural Lesion Analysis using DeepContrast

Dipika Sikka^1,2, Nanyan Zhu³, Chen Liu⁴, Scott Small⁵, and Jia Guo⁶

¹Department of Biomedical Engineering, Columbia University, New York, NY, United States, ²VantAI, New York, NY, United States, ³Department of Biological Sciences and the Taub Institute, Columbia University, New York, NY, United States, ⁴Department of Electrical Engineering and the Taub Institute, Columbia University, New York, NY, United States, ⁵Department of Neurology, the Taub Institute, the Sergievsky Center, Radiology and Psychiatry, Columbia University, New York, NY, United States, ⁶Department of Psychiatry, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States

Gadolinium-based contrast agents (GBCAs) have facilitated an improved analysis and understanding of structural lesions, however, present safety risks due to the tissue retention of GBCAs. Here we optimize and apply the deep learning model, DeepContrast, to predict gadolinium uptake in brain and breast structural lesions for structural lesion enhancement. The optimized DeepContrast models predict gadolinium uptake that is comparable to ground-truth scans consisting of the uptake from the GBCAs, using a single T1-weighted pre-contrast scan.

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