Semi-Supervised Learning of MRI Synthesis Without Fully-Sampled Ground Truths

Yurt M., Dalmaz O., Dar S., Ozbey M., Tinaz B., Oguz K., ...More

IEEE Transactions on Medical Imaging, vol.41, no.12, pp.3895-3906, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 41 Issue: 12
  • Publication Date: 2022
  • Doi Number: 10.1109/tmi.2022.3199155
  • Journal Name: IEEE Transactions on Medical Imaging
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, Compendex, EMBASE, INSPEC, MEDLINE, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.3895-3906
  • Keywords: Magnetic resonance imaging, image synthesis, semi-supervised, adversarial, undersampled
  • Hacettepe University Affiliated: Yes


© 1982-2012 IEEE.Learning-based translation between MRI contrasts involves supervised deep models trained using high-quality source- and target-contrast images derived from fully-sampled acquisitions, which might be difficult to collect under limitations on scan costs or time. To facilitate curation of training sets, here we introduce the first semi-supervised model for MRI contrast translation (ssGAN) that can be trained directly using undersampled k-space data. To enable semi-supervised learning on undersampled data, ssGAN introduces novel multi-coil losses in image, k-space, and adversarial domains. The multi-coil losses are selectively enforced on acquired k-space samples unlike traditional losses in single-coil synthesis models. Comprehensive experiments on retrospectively undersampled multi-contrast brain MRI datasets are provided. Our results demonstrate that ssGAN yields on par performance to a supervised model, while outperforming single-coil models trained on coil-combined magnitude images. It also outperforms cascaded reconstruction-synthesis models where a supervised synthesis model is trained following self-supervised reconstruction of undersampled data. Thus, ssGAN holds great promise to improve the feasibility of learning-based multi-contrast MRI synthesis.