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Abstract #1114

Predicting Delayed Phase Contrast-Enhanced MR Images from Early Phase Contrast-Enhanced MR Images Using Deep Learning-Based Iterative Network

Woojin Chung1, Junghwa Kang1, Ga Eun Park2, Sung Hun Kim2, and Yoonho Nam1
1Department of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin-si, Korea, Republic of, 2Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea, Republic of

Synopsis

Keywords: Analysis/Processing, Breast

Motivation: In breast MRI, early-phase contrast enhancement trends are key indicators for differentiating malignant from benign tissue. However, traditional imaging methods require extended time to capture these dynamic changes.

Goal(s): This study proposed a deep-neural network to map time-dependent information in contrast-enhanced breast MRI, enabling the temporal prediction of enhancement and aiming for abbreviated scans.

Approach: We designed an iterative network to sequentially generate post-contrast breast MR images at delayed time points, leveraging pre-contrast and early phase contrast-enhanced images.

Results: Results from fibroglandular tissue and tumor regions demonstrated the potential of our method for predicting the delayed phase image from the early phase image.

Impact: By enabling dynamic contrast prediction in breast MRI, our method aids in the characterization of enhancement patterns in breast tissue using only early phase post-contrast images. This approach potentially reduces scan times for dynamic contrast-enhanced MR applications.

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Keywords