U-Net and GAN for Virtual Contrast in Breast MRI: How Do They Compare to Real Contrast Images?

George A, Schreiter H, Hoßbach J, Nguyen TT, Horishnyi I, Ehring C, Heidarikahkesh S, Kapsner L, Laun FB, Uder M, Ohlmeyer S, Bickelhaupt S, Liebert A (2025)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2025

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer

Series: Informatik aktuell

City/Town: Cham

Pages Range: 277-282

Conference Proceedings Title: Bildverarbeitung für die Medizin 2025. Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025

Event location: Regensburg DE

ISBN: 9783658474218

DOI: 10.1007/978-3-658-47422-5_63

Abstract

Multiparametric MRI with gadolinium-based contrast agents demonstrates high sensitivity for detecting lesions in the breast, particularly in women with denser breast tissue. However, its use is limited by increased costs, time and contraindications in certain patients. This study explores a pix2pix generative adversarial network (GAN) to create virtual contrast-enhanced (vCE) MRI from un-enhanced T1w, T2w, and DWI sequences and compares it with a U-Net model. The vCE GAN achieved an SSIM of 80.75 and PSNR of 21.90, while the vCE U-Net scored 87.39 and 24.39, respectively. A multi-reader Turing test showed that 45.89% of vCE GAN images were rated as real, comparable to 45.09% for true CE images. In contrast, 47.25% of vCE U-Net images were rated as real.

Authors with CRIS profile

Hannes Schreiter Institute of Radiology Julian Hoßbach Institute of Radiology Tri-Thien Nguyen Lehrstuhl für Informatik 5 (Mustererkennung) Shirin Heidarikahkesh Institute of Radiology Lorenz Kapsner Institute of Radiology Frederik Bernd Laun Professur für quantitative MR-Bildgebung Michael Uder Lehrstuhl für Diagnostische Radiologie Sabine Ohlmeyer Institute of Radiology Sebastian Bickelhaupt Institute of Radiology Andrzej Liebert Institute of Radiology

How to cite

APA:

George, A., Schreiter, H., Hoßbach, J., Nguyen, T.-T., Horishnyi, I., Ehring, C.,... Liebert, A. (2025). U-Net and GAN for Virtual Contrast in Breast MRI: How Do They Compare to Real Contrast Images? In Christoph Palm, Katharina Breininger, Thomas Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Thomas M. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2025. Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025 (pp. 277-282). Regensburg, DE: Cham: Springer.

MLA:

George, Aju, et al. "U-Net and GAN for Virtual Contrast in Breast MRI: How Do They Compare to Real Contrast Images?" Proceedings of the German Conference on Medical Image Computing, 2025, Regensburg Ed. Christoph Palm, Katharina Breininger, Thomas Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Thomas M. Tolxdorff, Cham: Springer, 2025. 277-282.

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