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Abstract #0411

Deep Learning Framework for Gadolinium-free MR Imaging

Parisima Abdali1,2, Yao Wang2, Li Feng1, and Lavanya Umapathy1
1Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University, New York, NY, United States, 2Electrical and Computer Engineering Department at Tandon School of Engineering, New York University, New York, NY, United States

Synopsis

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Contrast Synthesis, Gadolinium-free, Brain

Motivation: Potential health risks, higher costs, and longer acquisition times associated with gadolinium (Gd)-enhanced MR imaging underscore the need for alternatives.

Goal(s): We leverage shared tissue-specific information from unenhanced multi-contrast MR images (T1w, T2w, and FLAIR) to synthesize high-quality T1-weighted contrast-enhanced images using deep learning.

Approach: A model is pretrained with self-supervised contrastive learning to capture local tissue-specific MR contrast information from unenhanced scans. The representations from T1w, T2w, and FLAIR images then enable synthesis of gadolinium-free T1-weighted images.

Results: Integrating tissue-specific MR contrast information in the synthesis of contrast-enhanced images improved image-quality evaluation metrics such as SSIM, PSNR, and LPIPS.

Impact: Our approach leverages multi-contrast MR images to capture tissue-specific information through a self-supervised contrastive learning framework. By synthesizing gadolinium-free T1-weighted images from unenhanced scans, we aim to retain diagnostic quality while potentially reducing the related risks, costs, and acquisition times.

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Keywords