Dynamic Human Brain Imaging with a Portable PET Camera: Comparison to a Standard Scanner

Elizabeth A Bartlett; Mohammad Lesanpezeshki; Sergey Anishchenko; Ilia Shkolnik; R Todd Ogden; J John Mann; David Beylin; Jeffrey M Miller; Francesca Zanderigo

doi:10.2967/jnumed.122.265309

Dynamic Human Brain Imaging with a Portable PET Camera: Comparison to a Standard Scanner

J Nucl Med. 2024 Feb 1;65(2):320-326. doi: 10.2967/jnumed.122.265309.

Authors

Elizabeth A Bartlett^{1

2}, Mohammad Lesanpezeshki³, Sergey Anishchenko⁴, Ilia Shkolnik⁴, R Todd Ogden^{3

2

5}, J John Mann^{3

2

6}, David Beylin⁴, Jeffrey M Miller^{3

2}, Francesca Zanderigo^{3

2}

Affiliations

¹ Molecular Imaging and Neuropathology Area, New York State Psychiatric Institute, New York, New York; elizabeth.bartlett@nyspi.columbia.edu.
² Department of Psychiatry, Columbia University Medical Center, New York, New York.
³ Molecular Imaging and Neuropathology Area, New York State Psychiatric Institute, New York, New York.
⁴ Brain Biosciences, Inc., Rockville, Maryland.
⁵ Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York; and.
⁶ Department of Radiology, Columbia University Medical Center, New York, New York.

PMID: 38124218
PMCID: PMC10858383 (available on 2024-08-01)
DOI: 10.2967/jnumed.122.265309

Abstract

Portable, cost-effective PET cameras can radically expand the applicability of PET. We present here a within-participant comparison of fully quantified [¹⁸F]FDG dynamic scans in healthy volunteers using the standard Biograph mCT scanner and portable CerePET scanner. Methods: Each of 20 healthy volunteers underwent dynamic [¹⁸F]FDG imaging with both scanners (1-154 d apart) and concurrent arterial blood sampling. Tracer SUV, net influx rate (K_i), and the corresponding cerebral metabolic rate of glucose (CMR_glu) were quantified at regional and voxel levels. Results: At the regional level, CerePET outcome measure estimates within participants robustly correlated with Biograph mCT estimates in the neocortex, wherein the average Pearson correlation coefficients across participants ± SD were 0.83 ± 0.07 (SUV) and 0.85 ± 0.08 (K_i and CMR_glu). There was also strong agreement between CerePET and Biograph mCT estimates, wherein the average regression slopes across participants were 0.84 ± 0.17 (SUV), 0.83 ± 0.17 (K_i), and 0.85 ± 0.18 (CMR_glu). There was similar bias across participants but higher correlation and less variability in subcortical regions than in cortical regions. Pearson correlation coefficients for subcortical regions equaled 0.97 ± 0.02 (SUV) and 0.97 ± 0.03 (K_i and CMR_glu), and average regression slopes equaled 0.79 ± 0.14 (SUV), 0.83 ± 0.11 (K_i), and 0.86 ± 0.11 (CMR_glu). In voxelwise assessment, CerePET and Biograph mCT estimates across outcome measures were significantly different only in a cluster of left frontal white matter. Conclusion: Our results indicate robust correlation and agreement between semi- and fully quantitative brain glucose metabolism measurements from portable CerePET and standard Biograph mCT scanners. The results obtained with a portable PET scanner in this comparison in humans require follow-up but lend confidence to the feasibility of more flexible and portable brain imaging with PET.

Keywords: SUV; human; metabolic rate of glucose; net influx rate; portable PET.

MeSH terms

Fluorodeoxyglucose F18*
Glucose / metabolism
Humans
Neocortex* / metabolism
Neuroimaging
Positron-Emission Tomography / methods

Substances

Fluorodeoxyglucose F18
Glucose