Generalized PSF modeling for optimized quantitation in PET imaging

Saeed Ashrafinia, Hassan Mohy-Ud-Din, Nicolas A. Karakatsanis, Abhinav Kumar Jha, Michael E. Casey, Dan J. Kadrmas, Arman Rahmim

Research output: Contribution to journalArticle

Abstract

Point-spread function (PSF) modeling offers the ability to account for resolution degrading phenomena within the PET image generation framework. PSF modeling improves resolution and enhances contrast, but at the same time significantly alters image noise properties and induces edge overshoot effect. Thus, studying the effect of PSF modeling on quantitation task performance can be very important. Frameworks explored in the past involved a dichotomy of PSF versus no-PSF modeling. By contrast, the present work focuses on quantitative performance evaluation of standard uptake value (SUV) PET images, while incorporating a wide spectrum of PSF models, including those that under- and over-estimate the true PSF, for the potential of enhanced quantitation of SUVs. The developed framework first analytically models the true PSF, considering a range of resolution degradation phenomena (including photon non-collinearity, inter-crystal penetration and scattering) as present in data acquisitions with modern commercial PET systems. In the context of oncologic liver FDG PET imaging, we generated 200 noisy datasets per image-set (with clinically realistic noise levels) using an XCAT anthropomorphic phantom with liver tumours of varying sizes. These were subsequently reconstructed using the OS-EM algorithm with varying PSF modelled kernels. We focused on quantitation of both SUVmean and SUVmax, including assessment of contrast recovery coefficients, as well as noise-bias characteristics (including both image roughness and coefficient of-variability), for different tumours/iterations/PSF kernels. It was observed that overestimated PSF yielded more accurate contrast recovery for a range of tumours, and typically improved quantitative performance. For a clinically reasonable number of iterations, edge enhancement due to PSF modeling (especially due to over-estimated PSF) was in fact seen to lower SUVmean bias in small tumours. Overall, the results indicate that exactly matched PSF modeling does not offer optimized PET quantitation, and that PSF overestimation may provide enhanced SUV quantitation. Furthermore, generalized PSF modeling may provide a valuable approach for quantitative tasks such as treatment-response assessment and prognostication.

Original languageEnglish (US)
Pages (from-to)5149-5179
Number of pages31
JournalPhysics in Medicine and Biology
Volume62
Issue number12
DOIs
StatePublished - May 26 2017

    Fingerprint

Keywords

  • partial volume correction
  • PET
  • PSF modeling
  • quantitation
  • standard uptake value (SUV)

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

Cite this

Ashrafinia, S., Mohy-Ud-Din, H., Karakatsanis, N. A., Jha, A. K., Casey, M. E., Kadrmas, D. J., & Rahmim, A. (2017). Generalized PSF modeling for optimized quantitation in PET imaging. Physics in Medicine and Biology, 62(12), 5149-5179. https://doi.org/10.1088/1361-6560/aa6911