Compressed Sensing for Moving Imagery in Medical Imaging

TL;DR

This paper introduces a motion compensating prior for compressed sensing in medical imaging, enabling better reconstruction of moving images by incorporating motion information directly into the regularization process.

Contribution

It proposes a novel regularization method based on optical flow for compressed sensing of moving images, allowing joint or separate estimation of signal and motion.

Findings

Improved reconstruction quality in MRI of moving objects.

Effective joint and separate signal-motion estimation demonstrated.

Applicable to various imaging scenarios involving motion.

Abstract

Numerous applications in signal processing have benefited from the theory of compressed sensing which shows that it is possible to reconstruct signals sampled below the Nyquist rate when certain conditions are satisfied. One of these conditions is that there exists a known transform that represents the signal with a sufficiently small number of non-zero coefficients. However when the signal to be reconstructed is composed of moving images or volumes, it is challenging to form such regularization constraints with traditional transforms such as wavelets. In this paper, we present a motion compensating prior for such signals that is derived directly from the optical flow constraint and can utilize the motion information during compressed sensing reconstruction. Proposed regularization method can be used in a wide variety of applications involving compressed sensing and images or volumes of moving and deforming objects. It is also shown that it is possible to estimate the signal and the motion jointly or separately. Practical examples from magnetic resonance imaging has been presented to demonstrate the benefit of the proposed method.