Assessing reliability of myocardial blood flow after motion correction with dynamic PET using a Bayesian framework

The estimation of myocardial blood flow (MBF) in dynamic PET can be biased by many different processes. A major source of error, particularly in clinical applications, is patient motion. Patient motion, or gross motion, creates displacements between different PET frames as well as between the PET frames and the CT-derived attenuation map, leading to errors in MBF calculation from voxel time series. Motion correction techniques are challenging to evaluate quantitatively and the impact on MBF reliability is not fully understood. Most metrics, such as signal-to-noise ratio (SNR), are characteristic of static images, and are not specific to motion correction in dynamic data. This study presents a new approach of estimating motion correction quality in dynamic cardiac PET imaging. It relies on calculating a MBF surrogate, K 1 , along with the uncertainty on the parameter. This technique exploits a Bayesian framework, representing the kinetic parameters as a probability distribution, from which the uncertainty measures can be extracted. If the uncertainty extracted is high, the parameter studied is considered to have high variability - or low confidence - and vice versa. The robustness of the framework is evaluated on simulated time activity curves to ensure that the uncertainties are consistently estimated at the multiple levels of noise. Our framework is applied on 40 patient datasets, divided in 4 motion magnitude categories. Experienced observers manually realigned clinical datasets with 3D translations to correct for motion. K 1 uncertainties were compared before and after correction. A reduction of uncertainty after motion correction of up to 60% demonstrates the benefit of motion correction in dynamic PET and as well as provides evidence of the usefulness of the new method presented