Adversarially learned iterative reconstruction for imaging inverse problems
This addresses the problem of limited labeled data for researchers and practitioners in medical imaging, offering an incremental improvement over existing unsupervised techniques.
The paper tackles the challenge of unsupervised learning for ill-posed inverse problems like medical image reconstruction, where ground-truth data is scarce, by proposing an adversarially learned iterative method that matches output distributions to ground-truth. It demonstrates competitive performance with supervised methods, avoiding over-smoothing while maintaining similar reconstruction times after training.
In numerous practical applications, especially in medical image reconstruction, it is often infeasible to obtain a large ensemble of ground-truth/measurement pairs for supervised learning. Therefore, it is imperative to develop unsupervised learning protocols that are competitive with supervised approaches in performance. Motivated by the maximum-likelihood principle, we propose an unsupervised learning framework for solving ill-posed inverse problems. Instead of seeking pixel-wise proximity between the reconstructed and the ground-truth images, the proposed approach learns an iterative reconstruction network whose output matches the ground-truth in distribution. Considering tomographic reconstruction as an application, we demonstrate that the proposed unsupervised approach not only performs on par with its supervised variant in terms of objective quality measures but also successfully circumvents the issue of over-smoothing that supervised approaches tend to suffer from. The improvement in reconstruction quality comes at the expense of higher training complexity, but, once trained, the reconstruction time remains the same as its supervised counterpart.