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Automated analysis of small animal PET studies through deformable registration to an atlas

Gutierrez, Daniel ; Zaidi, Habib

In: European Journal of Nuclear Medicine and Molecular Imaging, 2012, vol. 39, no. 11, p. 1807-1820

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    Title
    • Automated analysis of small animal PET studies through deformable registration to an atlas
    Author
    • Gutierrez, Daniel. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
    • Zaidi, Habib. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
    Document Type
    • Postprint
    Language
    • English
    Published in
    • European Journal of Nuclear Medicine and Molecular Imaging, 2012, vol. 39, no. 11, p. 1807-1820. Springer-Verlag
    Other Version
    OAI-PMH Identifier
    • oai:doc.rero.ch:313096
    Summary
    Purpose: This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model. Methods: A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed. Results: The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10% in most of the organs considered. Conclusion: The proposed automated quantification technique is reliable, robust and suitable for fast quantification of preclinical PET data in large serial studies