Data and Image Prior Integration for Image Reconstruction Using Consensus Equilibrium

TL;DR

This paper introduces a unified framework that combines data and image domain priors using consensus equilibrium, significantly enhancing image reconstruction quality in limited-data scenarios like CT and MRI.

Contribution

It extends the consensus equilibrium method to integrate physical sensor models, data models, and deep neural network priors for improved reconstruction.

Findings

Improved image quality in limited-angle CT and accelerated MRI.

Effective integration of data and image priors via consensus equilibrium.

Demonstrated on real and simulated datasets with superior results.

Abstract

Image domain prior models have been shown to improve the quality of reconstructed images, especially when data are limited. Pre-processing of raw data, through the implicit or explicit inclusion of data domain priors have separately also shown utility in improving reconstructions. In this work, a principled approach is presented allowing the unified integration of both data and image domain priors for improved image reconstruction. The consensus equilibrium framework is extended to integrate physical sensor models, data models, and image models. In order to achieve this integration, the conventional image variables used in consensus equilibrium are augmented with variables representing data domain quantities. The overall result produces combined estimates of both the data and the reconstructed image that is consistent with the physical models and prior models being utilized. The prior models used in both domains in this work are created using deep neural networks. The superior quality allowed by incorporating both data and image domain prior models is demonstrated for two applications: limited-angle CT and accelerated MRI. The prior data model in both these applications is focused on recovering missing data. Experimental results are presented for a 90 degree limited-angle tomography problem from a real checked-bagged CT dataset and a 4x accelerated MRI problem on a simulated dataset. The new framework is very flexible and can be easily applied to other computational imaging problems with imperfect data.