A feasibility study of joint respiratory and cardiac motion correction for coronary PET/CT imaging
Coronary artery disease (CAD) or atherosclerosis is a leading cause of death in industrialized nations. Such diseases are marked by development of chronic vascular inflammation in coronary arteries. Accurate assessment, characterization and localization of this inflammation through non-invasive methods is an important step towards the treatment of CAD. It has been shown that positron emission tomography (PET) is capable of detecting large vessel inflammation via activated macrophage uptake of FDG. However, respiratory and cardiac motion during image acquisition leads to severe blurring of the resulting images thereby rendering the spatial resolution inadequate for detection of inflammation in coronary arteries. The objective of this paper is to demonstrate the potential of producing high resolution PET images to enable imaging of coronary artery inflammation. In this paper, we propose a novel method for joint cardiac and respiratory motion correction in PET/CT called Cardiac Shape Tracking with Adjustment for Respiration (CSTAR). It uses a sequential cardiac and respiratory motion correction scheme by decoupling the two, and also features the use of all acquired data for SNR preservation. CT images are primarily used for cardiac shape tracking through the estimation of cardiac motion. Cardiac motion correction is incorporated in a super-resolution framework, followed by adjustment for the residual respiratory motion blur using blind deconvolution. We investigated the feasibility of this technique on simulated cardiac PET/CT data using XCAT and the preliminary results show a marked qualitative and quantitative improvement when compared to conventional PET reconstruction.