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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.

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U-Net and GAN for Virtual Contrast in Breast MRI

  • 会议论文

  • 发表日期：

    2025年

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  • Aju George,
  • Hannes Schreiter,
  • Julian Hossbach,
  • Tri-Thien Nguyen,
  • Ihor Horishnyi,
  • Chris Ehring,
  • Shirin Heidarikahkesh,
  • Lorenz A.Kapsner,
  • Frederik B.Laun,
  • Michael Uder,
  • Sabine Ohlmeyer,
  • Sebastian Bickelhaupt,
  • Andrzej Liebert

摘要

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.