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Suya Li

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Proceedings Article | 7 April 2023 Presentation + Paper

Optimal dynamic step and shoot temporal design for quantitative multi-organ dynamic PET imaging

Suya Li, Richard Laforest, Timothy Whitehead, Yuan-Chuan Tai, Kooresh Shoghi

Proceedings Volume 12463, 124631H (2023) https://doi.org/10.1117/12.2654467

KEYWORDS: Positron emission tomography, Biological imaging, Imaging systems, Scanners, Design and modelling, Received signal strength, Simulations, Matrices

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The recently developed total-body positron emission tomography (PET) scanner can capture total-body tracer kinetics, thus enabling systems imaging of multiple organs and interactions between organs, simultaneously. However, their prohibitive cost and sitting requirements may present a barrier for widespread adaptation. Most commercial PET scanners have a limited AFOV. To circumvent the limited AFOV, scanner manufacturers have implemented static step and shoot (SSS) protocols to “stitch” images acquired at multiple bed positions into a single total-body image. However, the resulting “total-body” images may not be quantitative depending on the kinetics of the tracers, thus biasing quantitative imaging comparisons. We propose a dynamic step and shoot (DSS) protocol with 2sec temporal sampling to pursue a continuous imaging protocol with different acquisition times in different bed positions for each pass through the torso. D-optimal criterion was used to optimize the acquisition protocol using a simulated annealing algorithm. The overall approach is illustrated in estimating parameters of a reversible two-compartment PET kinetic model for key organs in the torso. The intra- and inter-subject performance of the optimal DSS (ODSS) protocol was compared with the SSS protocol in terms of bias and variability and in comparison to the total-body (TB) protocol. The simulations suggest that the proposed ODSS protocol outperforms the conventional SSS protocol in both intra- and inter-subject parameter accuracy and precision tests and generates similar macro-parameter estimates compared to the TB scanner. Overall, we demonstrate that we can achieve an optimal temporal imaging schedule to support quantitative TB systems imaging.

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