Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion
  Probabilistic Model
Weijie Gan Huidong Xie Carl von Gall Günther Platsch Michael T. Jurkiewicz Andrea Andrade Udunna C. Anazodo Ulugbek S. Kamilov Hongyu An Jorge Cabello
Anatomically guided PET reconstruction using MRI information has been shown
to have the potential to improve PET image quality. However, these improvements
are limited to PET scans with paired MRI information. In this work we employed
a diffusion probabilistic model (DPM) to infer T1-weighted-MRI (deep-MRI)
images from FDG-PET brain images. We then use the DPM-generated T1w-MRI to
guide the PET reconstruction. The model was trained with brain FDG scans, and
tested in datasets containing multiple levels of counts. Deep-MRI images
appeared somewhat degraded than the acquired MRI images. Regarding PET image
quality, volume of interest analysis in different brain regions showed that
both PET reconstructed images using the acquired and the deep-MRI images
improved image quality compared to OSEM. Same conclusions were found analysing
the decimated datasets. A subjective evaluation performed by two physicians
confirmed that OSEM scored consistently worse than the MRI-guided PET images
and no significant differences were observed between the MRI-guided PET images.
This proof of concept shows that it is possible to infer DPM-based MRI imagery
to guide the PET reconstruction, enabling the possibility of changing
reconstruction parameters such as the strength of the prior on anatomically
guided PET reconstruction in the absence of MRI.