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A novel computational method for harmonization of brain PET image spatial resolution without phantoms

Felix Carbonell, Ph.D.1, Alex P. Zijdenbos, Ph.D.1, Evan Hempel 2, Mihály Hajós, Ph.D.2, Barry J. Bedell, M.D., Ph.D.1,3

1 Biospective Inc., Montréal, QC, Canada
2 Cognito Therapeutics, Inc., Cambridge, MA, USA
3 Research Institute of the McGill University Health Centre, Montréal, QC, Canada

The most common approach for estimating the spatial resolution of brain positron emission tomography (PET) images in multi-center studies typically uses Hoffman phantom data as a surrogate. Specifically, the phantom-based matching resolution approach assumes that scanned phantom PET images are well approximated by a ground truth, noise-free digital phantom convolved with a Gaussian kernel of unknown size. The size of the kernel is then estimated by an exhaustive search on the amount of blurring needed to match the smoothed digital phantom to a particular scanned phantom image.

Unfortunately, Hoffman phantom images may not always be readily available, and phantom-based approaches may yield sub-optimal results. To overcome these limitations, we propose a new, computational approach, called SPITFIRE™, that allows estimation of spatial resolution directly from the PET image itself. We generalized the so-called logarithmic intensity plots method to the 3D case to perform a spatial resolution estimation in both axial and in-plane directions of the PET images.

Our approach was applied to two different cohorts. The first cohort consisted of [18F]florbetapir amyloid PET images and matching phantoms coming from a Phase II clinical trial and includes different scanner models and/or orientation and grid reconstructions. The second cohort included β-amyloid, tau, and FDG PET images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study.

We obtained in-plane and axial resolution estimators that vary between 3.5 mm and 8.5 mm for both PET and matching phantom images. In both cases, we obtained small across-subject variability in groups of images sharing the same PET scanner model and reconstruction parameters. For human PET images, we also obtained a strong cross-tracer and longitudinal consistency in the spatial resolution estimators.

Our novel approach does not only eliminate the need for surrogate brain phantom data, but also provides a general framework that can be applied to a wide range of tracers and other image modalities, such as SPECT.

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