Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method.

Journal: IEEE transactions on radiation and plasma medical sciences
Published Date: Nov 30, 2018

Abstract

Anatomically-driven image reconstruction algorithms have become very popular in positron emission tomography (PET) where they have demonstrated improved image resolution and quantification. This work, consider the effect of spatial inconsistency between MR and PET images in hot and cold regions of the PET image. We investigate these effects on the kernel method from machine learning, in particular, the hybrid kernelized expectation maximization (HKEM). These were applied to Jaszczak phantom and patient data acquired with the Biograph Siemens mMR. The results show that even a small shift can cause a significant change in activity concentration. In general, the PET-MR inconsistencies can induce the partial volume effect, more specifically the 'spill-in' of the affected cold regions and the 'spill-out' from the hot regions. The maximum change was about 100% for the cold region and 10% for the hot lesion using KEM, against the 37% and 8% obtained with HKEM. The findings of this work suggest that including PET information in the kernel enhances the flexibility of the reconstruction in case of spatial inconsistency. Nevertheless, accurate registration and choice of the appropriate MR image for the creation of the kernel is essential to avoid artifacts, blurring, and bias.

Authors

  • Daniel Deidda
    Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, and the Department of Statistics, School of Mathematics, University of Leeds, UK.
  • Nicolas Karakatsanis
    Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, Department of Radiology, NY, USA; Division of Radio-pharmaceutical Sciences, Department of Radiology, Weill Cornell Medical College of Cornell University, NY, USA.
  • Philip M Robson
    Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, Department of Radiology, NY, USA.
  • Nikos Efthimiou
    School of Life Sciences, Faculty of Health Sciences, University of Hull, UK.
  • Zahi A Fayad
    Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, Department of Radiology, NY, USA.
  • Robert G Aykroyd
    Department of Statistics, School of Mathematics, University of Leeds, UK.
  • Charalampos Tsoumpas
    Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals