Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction

TL;DR

This paper introduces a unified supervised-unsupervised learning framework for X-ray CT image reconstruction, combining deep learning and analytical priors within a model-based iterative reconstruction approach, demonstrating superior results on low-dose CT data.

Contribution

It proposes a novel unified framework that integrates supervised deep priors with unsupervised or analytical priors in a model-based iterative reconstruction setting.

Findings

Supervised-unsupervised models outperform standalone methods.

The approach converges rapidly in practice.

Effective for low-dose CT image reconstruction.

Abstract

Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent machine learning methods for image reconstruction typically involve supervised learning or unsupervised learning, both of which have their advantages and disadvantages. In this work, we propose a unified supervised-unsupervised (SUPER) learning framework for X-ray computed tomography (CT) image reconstruction. The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis. The proposed training algorithm is also an approximate scheme for a bilevel supervised training optimization problem, wherein the network-based regularizer in the lower-level MBIR problem is optimized using an upper-level reconstruction loss. The training problem is optimized by alternating between updating the network weights and iteratively updating the reconstructions based on those weights. We demonstrate the learned SUPER models' efficacy for low-dose CT image reconstruction, for which we use the NIH AAPM Mayo Clinic Low Dose CT Grand Challenge dataset for training and testing. In our experiments, we studied different combinations of supervised deep network priors and unsupervised learning-based or analytical priors. Both numerical and visual results show the superiority of the proposed unified SUPER methods over standalone supervised learning-based methods, iterative MBIR methods, and variations of SUPER obtained via ablation studies. We also show that the proposed algorithm converges rapidly in practice.