We propose a generalized resolution modeling (RM) framework, including extensive task-based optimization,
wherein we continualize the conventionally discrete framework of RM vs. no RM, to include varying degrees of RM.
The proposed framework has the advantage of providing a trade-off between the enhanced contrast recovery by RM and
the reduced inter-voxel correlations in the absence of RM, and to enable improved task performance. The investigated
context was that of oncologic lung FDG PET imaging. Given a realistic blurring kernel of FWHM h (‘true PSF’), we
performed iterative EM including RM using a wide range of ‘modeled PSF’ kernels with varying widths h. In our
simulations, h = 6mm, while h varied from 0 (no RM) to 12mm, thus considering both underestimation and
overestimation of the true PSF. Detection task performance was performed using prewhitened (PWMF) and nonprewhitened
matched filter (NPWMF) observers. It was demonstrated that an underestimated resolution blur (h = 4mm)
enhanced task performance, while slight over-estimation (h = 7mm) also achieved enhanced performance. The latter is
ironically attributed to the presence of ringing artifacts. Nonetheless, in the case of the NPWMF, the increasing intervoxel
correlations with increasing values of h degrade detection task performance, and underestimation of the true PSF
provides the optimal task performance. The proposed framework also achieves significant improvement of
reproducibility, which is critical in quantitative imaging tasks such as treatment response monitoring.
KEYWORDS: Reconstruction algorithms, Positron emission tomography, Data modeling, Signal to noise ratio, Tumors, Image quality standards, Image enhancement, Blood, 3D modeling, Algorithm development
Graphical analysis is employed in the research setting to provide quantitative estimation of PET tracer kinetics from
dynamic images at a single bed. Recently, we proposed a multi-bed dynamic acquisition framework enabling clinically
feasible whole-body parametric PET imaging by employing post-reconstruction parameter estimation. In addition, by
incorporating linear Patlak modeling within the system matrix, we enabled direct 4D reconstruction in order to
effectively circumvent noise amplification in dynamic whole-body imaging. However, direct 4D Patlak reconstruction
exhibits a relatively slow convergence due to the presence of non-sparse spatial correlations in temporal kinetic analysis.
In addition, the standard Patlak model does not account for reversible uptake, thus underestimating the influx rate Ki. We
have developed a novel whole-body PET parametric reconstruction framework in the STIR platform, a widely employed
open-source reconstruction toolkit, a) enabling accelerated convergence of direct 4D multi-bed reconstruction, by
employing a nested algorithm to decouple the temporal parameter estimation from the spatial image update process, and
b) enhancing the quantitative performance particularly in regions with reversible uptake, by pursuing a non-linear
generalized Patlak 4D nested reconstruction algorithm.
A set of published kinetic parameters and the XCAT phantom were employed for the simulation of dynamic multi-bed
acquisitions. Quantitative analysis on the Ki images demonstrated considerable acceleration in the convergence of the nested 4D whole-body Patlak algorithm. In addition, our simulated and patient whole-body data in the postreconstruction
domain indicated the quantitative benefits of our extended generalized Patlak 4D nested reconstruction for
tumor diagnosis and treatment response monitoring.
We propose a novel framework of robust kinetic parameter estimation applied to absolute ow quanti cation in dynamic PET imaging. Kinetic parameter estimation is formulated as a nonlinear least squares with spatial constraints problem (NLLS-SC) where the spatial constraints are computed from a physiologically driven clustering of dynamic images, and used to reduce noise contamination. An ideal clustering of dynamic images depends on the underlying physiology of functional regions, and in turn, physiological processes are quanti ed by kinetic parameter estimation. Physiologically driven clustering of dynamic images is performed using a clustering algorithm (e.g. K-means, Spectral Clustering etc) with Kinetic modeling in an iterative handshaking fashion. This gives a map of labels where each functionally homogenous cluster is represented by mean kinetics (cluster centroid). Parametric images are acquired by solving the NLLS-SC problem for each voxel which penalizes spatial variations from its mean kinetics. This substantially reduces noise in the estimation process for each voxel by utilizing kinetic information from physiologically similar voxels (cluster members). Resolution degradation is also substantially minimized as no spatial smoothing between heterogeneous functional regions is performed. The proposed framework is shown to improve the quantitative accuracy of Myocardial Perfusion (MP) PET imaging, and in turn, has the long-term potential to enhance capabilities of MP PET in the detection, staging and management of coronary artery disease.
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