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Title
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Motion dependent and spatially variant resolution modeling for PET rigid motion correction
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Author
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Abstract
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| Recent advances in positron emission tomography (PET) have allowed to perform brain scans of freely moving animals by using rigid motion correction. One of the current challenges in these scans is that, due to the PET scanner spatially variant point spread function (SVPSF), motion corrected images have a motion dependent blurring since animals can move throughout the entire field of view (FOV). We developed a method to calculate the image-based resolution kernels of the motion dependent and spatially variant PSF (MD-SVPSF) to correct the loss of spatial resolution in motion corrected reconstructions. The resolution kernels are calculated for each voxel by sampling and averaging the SVPSF at all positions in the scanner FOV where the moving object was measured. In resolution phantom scans, the use of the MD-SVPSF resolution model improved the spatial resolution in motion corrected reconstructions and corrected the image deformation caused by the parallax effect consistently for all motion patterns, outperforming the use of a motion independent SVPSF or Gaussian kernels. Compared to motion correction in which the SVPSF is applied independently for every pose, our method performed similarly, but with more than two orders of magnitude faster computation time. Importantly, in scans of freely moving mice, brain regional quantification in motion-free and motion corrected images was better correlated when using the MD-SVPSF in comparison with motion independent SVPSF and a Gaussian kernel. The method developed here allows to obtain consistent spatial resolution and quantification in motion corrected images, independently of the motion pattern of the subject. |
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Language
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English
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Source (journal)
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IEEE transactions on medical imaging / Institute of Electrical and Electronics Engineers. - New York, N.Y.
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Publication
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New York, N.Y.
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2020
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ISSN
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0278-0062
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DOI
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10.1109/TMI.2019.2962237
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Volume/pages
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39
:7
(2020)
, p. 2518-2530
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ISI
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000545410200021
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Pubmed ID
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32070945
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Full text (Publisher's DOI)
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Full text (open access)
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