Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data

Abstract

Kinetic parameter estimation in dynamic PET suffers from reduced accuracy and precision when parametric maps are estimated using kinetic modelling following image reconstruction of the dynamic data. Direct approaches to parameter estimation attempt to directly estimate the kinetic parameters from the measured dynamic data within a unified framework. Such image reconstruction methods have been shown to generate parametric maps of improved precision and accuracy in dynamic PET. However, due to the interleaving between the tomographic and kinetic modelling steps, any tomographic or kinetic modelling errors in certain regions or frames, tend to spatially or temporally propagate. This results in biased kinetic parameters and thus limits the benefits of such direct methods. Kinetic modelling errors originate from the inability to construct a common single kinetic model for the entire field-of-view, and such errors in erroneously modelled regions could spatially propagate. Adaptive models have been used within 4D image reconstruction to mitigate the problem, though they are complex and difficult to optimize. Tomographic errors in dynamic imaging on the other hand, can originate from involuntary patient motion between dynamic frames, as well as from emission/transmission mismatch. Motion correction schemes can be used, however, if residual errors exist or motion correction is not included in the study protocol, errors in the affected dynamic frames could potentially propagate either temporally, to other frames during the kinetic modelling step or spatially, during the tomographic step. In this work, we demonstrate a new strategy to minimize such error propagation in direct 4D image reconstruction, focusing on the tomographic step rather than the kinetic modelling step, by incorporating time-of-flight (TOF) within a direct 4D reconstruction framework. Using ever improving TOF resolutions (580 ps, 440 ps, 300 ps and 160 ps), we demonstrate that direct 4D TOF image reconstruction can substantially prevent kinetic parameter error propagation either from erroneous kinetic modelling, inter-frame motion or emission/ transmission mismatch. Furthermore, we demonstrate the benefits of TOF in parameter estimation when conventional post-reconstruction (3D) methods are used and compare the potential improvements to direct 4D methods. Further improvements could possibly be achieved in the future by combining TOF direct 4D image reconstruction with adaptive kinetic models and interframe motion correction schemes.

Robustness of post-reconstruction and direct kinetic parameter estimates under rigid head motion in dynamic brain PET imaging

“…Comparing across the 2 parameter estimation methods, different levels of bias were observed. In accordance with our previous observations [24], direct parameter estimation resulted in additional bias. The increased bias in the direct 4D reconstruction appears to be related to the level of bias seen in the post-reconstruction analysis therefore potentially signifying propagation of bias between the tomographic and kinetic modelling steps.…”

“…Under such conditions, motion correction using either frame-by-frame methods or image reconstruction-based methods is highly recommended, especially when the protocol involves direct parameter estimation within a unified image reconstruction and kinetic modelling framework. In the absence of a motion correction scheme, motion-induced bias propagation could be limited by means of a time-of-flight based direct 4D image reconstruction [24]. However, bias reduction seen in thoracic and abdominal imaging can only be realized at substantially higher TOF time resolutions due to the difference in the diameter of the scanned object.…”

“…Direct parameter estimation methods have been shown to generate parametric images of improved accuracy and precision when used in brain imaging applications [21][22][23]. However, it has also been shown that when used in body imaging, motion-induced kinetic parameter errors tend to spatially propagate in the FOV during parameter estimation [24]. Therefore, it is of importance to investigate their behaviour in dynamic brain imaging given the different nature and magnitude of motion compared to abdominal imaging, as such algorithms have been consistently shown to supersede traditional post-reconstruction kinetic modelling approaches.…”

Consensus Recommendations on the Use of18F-FDG PET/CT in Lung Disease

“…Although TOF PET/CT scanners have become more widely available, insufficient data are available to make a firm recommendation on whether TOF is valuable for quantitative lung imaging. For multicenter studies, we recommend that either data from scanners with a similar TOF time resolution are used (44) or that only non-TOF reconstructed images are used for consistent results. Table 1 summarizes recommendations on patient preparation, acquisition protocol and image reconstruction.…”

High Temporal-Resolution Dynamic PET Image Reconstruction Using A New Spatiotemporal Kernel Method

“…However, this assumption is challenging to meet especially for new radiotracers and new clinical applications where the underlying kinetic model can be different from existing models. Any mismatch of kinetic model can induce significant bias propagation in the kinetic images [18].…”