Abstract
The most common approach for estimating the spatial resolution of 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. We propose a new, computational approach 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. The proposed 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, FDG and tau 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.
Competing Interest Statement
Author Felix Carbonell is an employee of Biospective Inc. Authors Alex Zijdenbos and Barry J. Bedell are shareholders of Biospective Inc. Mihaly Hajos and Evan Hempel are employees and own stock options in Cognito Therapeutics, Inc. Mihaly Hajos has patent applications assigned to Cognito Therapeutics, Inc.
Footnotes
↵* Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
