Image Reconstruction by Splitting Expectation Propagation Techniques from Iterative Inversion
This work addresses image reconstruction for medical imaging applications like Gamma-camera scans, but it appears incremental as it combines existing techniques (EP with MC, MCMC, ADMM) rather than introducing a fundamentally new approach.
The authors tackled image reconstruction from downsampled and noisy measurements like MRI and CT by proposing a method based on Expectation Propagation (EP) combined with Monte Carlo, MCMC, and ADMM to address intractability. Experiments on Gamma-camera scans showed the method is less computationally expensive than MCMC and produces relatively better image reconstruction.
Reconstructing images from downsampled and noisy measurements, such as MRI and low dose Computed Tomography (CT), is a mathematically ill-posed inverse problem. We propose an easy-to-use reconstruction method based on Expectation Propagation (EP) techniques. We incorporate the Monte Carlo (MC) method, Markov Chain Monte Carlo (MCMC), and Alternating Direction Method of Multiplier (ADMM) algorithm into EP method to address the intractability issue encountered in EP. We demonstrate the approach on complex Bayesian models for image reconstruction. Our technique is applied to images from Gamma-camera scans. We compare EPMC, EP-MCMC, EP-ADMM methods with MCMC only. The metrics are the better image reconstruction, speed, and parameters estimation. Experiments with Gamma-camera imaging in real and simulated data show that our proposed method is convincingly less computationally expensive than MCMC and produces relatively a better image reconstruction.