A PET supersets data framework for exploitation of known motion in image reconstruction

Motion during PET data acquisition is either introduced by design (e.g., couch wobble to enrich data sampling) or through unintentional motion of the object under study (e.g., subject head motion). Prior to this present work, such effects have been considered within distinctly different frameworks in PET imaging, with a rather basic approach having been devised for the cases of intentional motion (such as couch wobble or bed translation) in contrast to the relatively advanced modeling approaches devised for object motion. This article unifies the treatment of these different types of acquisition motion within a generalized framework through the use of the PET supersets data format. From this general framework, a range of conventional motion-compensation methods can be examined as special cases, permitting a revealing investigation into both the suboptimality of commonly adopted approximations, as well as the beneficial impact of known acquisition motion on image quality.

The PET superset data format is a data representation ideally suited for the (potentially lossless) combination of conventional PET data with complementary motion information. Different practical implementations for the reconstruction from PET superset data involving varying levels of approximation to facilitate computation are identified and their impact on the final reconstructed image quality is assessed. Three main simulated case studies are investigated: (i) Motion compensation for subject head motion for 18F-FDG imaging, (ii) motion exploitation using either couch wobble or random motion, and (iii) motion exploitation using involuntary object motion both with and without couch wobble occurring. The motion exploitation case study goes beyond merely correcting for motion: The motion is directly modeled by the iterative reconstruction to exploit the increased sampling which is available to the benefit of reconstructed image quality.

Reconstruction from superset data was successfully demonstrated for the case of motion correction for a 18F-FDG brain phantom simulation. Building on this success, the methodology was then applied to the case of motion exploitation. The case study resulted in three important findings. First, only reconstruction implementations which model the motion directly within the iterative reconstruction can succeed in significantly improving image resolution and contrast recovery for a given reconstructed noise level. Therefore, the commonly adopted approximation of binning the motion-adjusted data (which is the only method reported to date in the literature) is suboptimal and underestimates the beneficial impact of methods such as wobble during acquisition. Second, similar improvements were found for both types of motion patterns: Periodic wobble motion as well as random motion. Finally, for the case of simulated realistic involuntary object motion, similar resolution improvements were found (both with and without couch wobble).

The proposed superset framework allows comprehensive analysis of the commonly adopted approximations when considering motion in PET reconstruction. The findings demonstrate that the supersets data format successfully unify reconstruction in the presence of different sources of known acquisition motion into one framework, and as a result leads to hitherto unreported image quality improvements for all the cases tested where the object motion is accurately known.