A Feasibility Study of Joint Respiratory and Cardiac Motion Correction for Coronary PET/CT Imaging.
ABSTRACT 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.
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ABSTRACT: Coronary artery disease is marked by the development of chronic inflammation in the vascular arteries that is associated with coronary plaques. Positron emission tomography (PET) is capable of detecting inflammation through activated macrophage uptake of FDG. Unfortunately, in conventional cardiac PET, respiratory and cardiac motion during acquisition leads to severe blurring of the resulting images and an effective spatial resolution inadequate for plaque detection and localization. In this paper, we extend our previous image-domain approach to a fully integrated, data-domain method that starts from the observed projection data and performs a model-based inversion and motion correction of all the data to create a high-resolution focused cardiac image. We term the new approach Data-domain Cardiac Shape Tracking and Adjustment for Respiration or D-CSTAR. In contrast to existing image domain methods the image reconstruction and motion correction steps are not separated. Unlike current data domain methods both cardiac and respiratory motions are compensated for. In D-CSTAR, cardiac motion parameters are estimated from X-ray CT images acquired in a breath-hold state. This cardiac motion information is incorporated in a unified PET reconstruction functional which jointly estimates and corrects for respiratory motion, compensates for phase aligned cardiac motion, and super-resolves the image. The technique is presented and applied to simulated cardiac PET/CT data corresponding to the XCAT phantom with both cardiac and respiratory cycles. The results show a marked qualitative and quantitative improvement when compared to conventional and existing PET methods.Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011