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Abstract #3243

AI-based Synthetic Contrast-enhanced MR Images from Noncontrast MR Images for Various Brain Tumors beyond Gliomas

Hirotaka Takita1, Hiroyuki Tatekawa1, Kosuke Nakajo2, Takehiro Uda2, Yasuhito Mitsuyama1, Shannon L Walston1, Yukio Miki1, and Daiju Ueda1,3
1Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan, 2Department of Neurosurgery, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan, 3Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan

Synopsis

Keywords: Other AI/ML, Machine Learning/Artificial Intelligence, Neuro, Tumors, synthetic contrast-enhanced MR images

Motivation: Contrast-enhanced MR images with gadolinium-based contrast agents is crucial for brain tumor evaluation, but contraindicated in some patients. AI-based synthetic contrast-enhanced MR images offer an alternative, though studies have mainly focused on gliomas.

Goal(s): To generate synthetic contrast-enhanced MR images from noncontrast MR images across various brain tumor types using multi-center datasets.

Approach: A 3D-UNet-based AI model was developed using noncontrast T1-weighted and T2-weighted images as input and contrast-enhanced T1-weighted images as target.

Results: Synthetic contrast-enhanced MR images demonstrated high SSIM and PSNR across various brain tumor types, with significant correlation of tumor contrast-enhanced volume compared to real ones.

Impact: Our study suggests potential clinical applicability of AI-based synthetic contrast-enhanced MR images generated from noncontrast MR images across various brain tumor types, offering a promising alternative for patients unable to receive gadolinium-based contrast agents.

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Keywords