PET Imaging of the Human Brain with Microvolumetric Spatial Resolution
Bibliographic record
Abstract
PET is the modality of choice for studying the biochemistry and physiology of the human brain in vivo, although its low spatial resolution, attributable to inherent physical and technical constraints, has limited its ability to resolve small cerebral structures. Here, we report PET images of the human brain obtained at a volumetric resolution of nearly 2 µL. Methods: A dedicated ultra-high-resolution (UHR) PET scanner featuring 1.2-mm true pixelated detectors was developed to achieve microvolumetric spatial resolution. A partially assembled UHR PET scanner with an axial field of view of 143 mm was used to obtain 18F-FDG PET images of the human brain. Patients who had a clinical PET/CT scan subsequently underwent UHR PET on completion of their medical examination. UHR PET images were reconstructed using a 3-dimensional ordered-subset expectation maximization iterative algorithm with analytic coincidence function modeling. Reconstructed images were normalized to the Montreal Neurological Institute 152 brain template and analyzed using atlases for region identification. Relative SUVs to the cerebellum were extracted for selected small brain structures. Results: All major brain regions were easily identifiable in UHR PET images, including details of the primary motor and somatosensory cortices, caudate nucleus, putamen, thalamus, inferior colliculi, and dentate nuclei. Notably, regions rarely seen so distinctly with 18F-FDG PET, such as the subthalamic areas and brainstem nuclei, were successfully resolved, suggesting that UHR PET has the potential to provide enhanced quantification of these tiny cerebral structures. This was further confirmed by higher SUV ratios in the UHR PET images compared with the PET/CT images. The UHR PET image of 1 patient revealed hypermetabolic foci in the cerebellum that were not discernible on the PET/CT and MR images. Conclusion: UHR PET images of the human brain at nearly 2-µL volumetric spatial resolution were obtained. Previously indistinguishable, small, highly relevant regions of the brain were resolved, paving the way for more accurate and detailed studies with the potential for greater insight in neuropsychiatry, neurooncology, and neurodegenerative diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".