Abstract
The separation of multiple PET tracers within an overlapped scan based on intrinsic difference of pharmacokinetics is challenging due to the limited SNR of PET measurements and high complexity of fitting models. This study developed a novel direct parametric reconstruction method by integrating a multi-tracer model with reduced number of fitting parameters into image reconstruction. To incorporate the multi-tracer model, we adopted EM surrogate functions for the optimization of the penalized log-likelihood. The algorithm was validated on realistic simulation phantoms and real rapid [18F]FDG and [18F]FLT PET imaging of mice with lymphoma mouse tumor. Both results have been compared with conventional methods and demonstrated evident improvements for the separation of multiple tracers.
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Keywords
- Arterial Input Function
- Single Tracer
- Multiple Tracer
- Penalize Likelihood Estimation
- Expectation Maximization Reconstruction
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Cheng, X., Navab, N., Ziegler, S.I., Shi, K. (2013). Direct Parametric Image Reconstruction of Rapid Multi-tracer PET. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_20
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DOI: https://doi.org/10.1007/978-3-642-40760-4_20
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