Magnetic Resonance Materials in Physics, Biology and Medicine

, Volume 22, Issue 4, pp 257–265

Monitoring the survival of islet transplants by MRI using a novel technique for their automated detection and quantification

  • Daniel Jirak
  • Jan Kriz
  • Michal Strzelecki
  • Jiabi Yang
  • Craig Hasilo
  • David J. White
  • Paula J. Foster
Research Article

DOI: 10.1007/s10334-009-0172-4

Cite this article as:
Jirak, D., Kriz, J., Strzelecki, M. et al. Magn Reson Mater Phy (2009) 22: 257. doi:10.1007/s10334-009-0172-4

Abstract

Object

There is a clinical need to be able to assess graft loss of transplanted pancreatic islets (PI) non-invasively with clear-cut quantification of islet survival. We tracked transplanted PI in diabetic mice during the early post-transplant period by magnetic resonance imaging (MRI) and quantified the islet loss using automatic segmentation technique.

Materials and methods

Magnetically labeled islet iso-, allo- and xenografts were injected into the right liver lobes. Animals underwent MRI scanning during 14 days after PI transplantation. MR images were processed using custom-made software, which automatically detects hypointense regions representing PI. It is based on morphological top-hat and bottom-hat transforms.

Results

Manually and automatically detected areas, corresponding to PI, differed by 4% in phantoms. Signal loss regions due to PI decreased comparably in all groups during the first week post transplant. Throughout the second week post-transplant, the signal loss area continued in a steep decline in case of allografts and xenografts, whereas the decline in case of isografts slowed down.

Conclusion

Automatic segmentation allows for the more reproducible, objective assessment of transplanted PI. Quantification confirms the assumption that a significant number of islets are destroyed in the first week following transplantation irrespective of allografts, xenografts or isografts.

Keywords

Magnetic resonance imaging (MRI)Pancreatic islets (PI)QuantificationAutomatic segmentationAnimal model

Copyright information

© ESMRMB 2009

Authors and Affiliations

  • Daniel Jirak
    • 1
  • Jan Kriz
    • 1
    • 2
  • Michal Strzelecki
    • 3
  • Jiabi Yang
    • 2
  • Craig Hasilo
    • 2
  • David J. White
    • 2
  • Paula J. Foster
    • 1
    • 4
  1. 1.Imaging Research LaboratoriesRobarts Research InstituteLondonCanada
  2. 2.Transplantation GroupRobarts Research InstituteLondonCanada
  3. 3.Institute of ElectronicsTechnical University of LodzLodzPoland
  4. 4.Department of Medical BiophysicsUniversity of Western OntarioLondonCanada