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Abstract #2560

Deep Learning-Based Prediction of Gadolinium-Enhanced MRI from Non-Contrast MRI and MR Fingerprinting

Eunate Alzaga Goñi1, Walter Zhao1, Sree Gongala2, Rhea Adams1, Shahrzad Moinian1, Shengwen Deng2, Parisa Arjmand2, Yong Chen2, Chaitra Badve2, and Dan Ma1
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States

Synopsis

Keywords: Tumors (Pre-Treatment), Tumors, Artificial Intelligence, MR Fingerprinting

Motivation: Gadolinium-enhanced MRI is crucial for brain tumor diagnosis, but drawbacks associated with gadolinium-based contrast agents (GBCAs) drive interest in developing alternative imaging methods.

Goal(s): Our goal is to develop a model capable of predicting gadolinium-enhanced MR images that closely resemble those obtained with contrast injection, in terms of tumor enhancement overlap and overall appearance.

Approach: We developed a deep-learning based model that can be trained with pre-contrast MRF maps, synthetic contrasts, and/or clinical images and predict gadolinium-enhanced MR images.

Results: The models obtained competitive enhancing tumor overlap values (0.599, 0.603, 0.622) and quantitative metrics (average SSIM=0.944, CC=0.941, and MAE=0.261).

Impact: The clinical use of a deep learning model to obtain gadolinium-enhanced MR images would replace the need for GBCAs, thus eliminating associated problems including longer scan times, higher costs, and certain patient risks.

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Keywords