Deformation-Compensated Learning for Image Reconstruction without Ground Truth

Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov

Research output: Contribution to journalArticlepeer-review


Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets. Recent work on Noise2Noise (N2N) has shown the potential of using multiple noisy measurements of the same object as an alternative to having a ground-truth. However, existing N2N-based methods are not suitable for learning from the measurements of an object undergoing nonrigid deformation. This paper addresses this issue by proposing the deformation-compensated learning (DeCoLearn) method for training deep reconstruction networks by compensating for object deformations. A key component of DeCoLearn is a deep registration module, which is jointly trained with the deep reconstruction network without any ground-truth supervision. We validate DeCoLearn on both simulated and experimentally collected magnetic resonance imaging (MRI) data and show that it significantly improves imaging quality.

Original languageEnglish
JournalIEEE Transactions on Medical Imaging
StateAccepted/In press - 2022


  • Convolutional neural networks
  • deep learning
  • image reconstruction
  • Image reconstruction
  • Imaging
  • Inverse problems
  • Magnetic resonance imaging
  • magnetic resonance imaging (MRI)
  • Noise measurement
  • Strain
  • Training


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