Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

2 Publications

Automated objective optimization of iterative image reconstruction protocols

Nikulin, P.; Maus, J.; Hofheinz, F.; van den Hoff, J.

The image quality achieved in iterative PET image reconstruction is influenced by several internal and user-settable parameters (number of iterations and subsets, PSF model, etc.). Typically, there are more than 3 user-settable parameters involved, interacting in a non-intuitive way. Reasonable settings typically are obtained interactively by try-and-error which is highly subjective. This proof-of-concept work proposes a method for automated reconstruction parameters optimization for a given, preselected image quality metric.

In out approach, we reconstruct images of cylindrical phantom with six "hot" sphere inserts simulating lesions of different sizes and target-to-background activity concentration ratios (20:1, 10:1, 5:1). 4 parameters of our in-house reconstruction tool THOR [1] were varied during optimization: no. of iterations and subsets, tube of response (ToR) radius, Gaussian post filter FWHM. As image quality metric we chose the weighted sum of standard deviation of contrast recovery coefficients of all 6 inserts (as a surrogate for image resolution), the image noise, and Gibbs artifacts. This metric is minimized with Bayesian optimization method using Gaussian process as a surrogate function. The reconstruction parameters resulting in the minimum metric value were chosen.

The optimization process lasted for 50 iterations. The resulting reconstruction parameters were: no. of iterations/subsets=2/21, ToR radius=2.95mm, Gaussian filter FWHM=4.0mm. The resulting images show [4.7-5.8]mm resolution and 14% noise level. Gibbs artifacts level was found to be below 3.5%.

Our framework for reconstruction protocol optimization is capable of deriving reasonable reconstruction parameters in a fully automated manner. The presented approach might also be used to improve and objectify the comparison of different image reconstruction algorithms.


[1] A. Lougovski, et al., Physics in Medicine and Biology, vol.59(3), p.561, 2014

Keywords: PET; Iterative image reconstruction; THOR; MLEM; Bayesian optimization

  • Lecture (Conference) (Online presentation)
    NuklearMedizin 2021 – hybrid, 14.-17.04.2021, Online, Online
  • Contribution to proceedings
    NuklearMedizin 2021 – hybrid, 14.-17.04.2021, Online, Online
    Nuklearmedizin 2021; 60(02): 151, eRef: Thieme Publishing Group
    DOI: 10.1055/s-0041-1726740

Publ.-Id: 32711