The BrainPET-7T Insert for Neuroscientific Applications: Performance with and without MRI
Bibliographic record
Abstract
We present the performance of a high-resolution, high-sensitivity, and UHF-MRI-compatible BrainPET insert for in vivo multimodal and multi-parametric neuroscientific imaging applications in humans. In this performance evaluation we studied mutual compatibility, PET-relevant imaging parameters, (spatial resolution, sensitivity, noise equivalent count rate, and coincidence time resolution), and MRI-relevant imaging parameters, (image homogeneity, image SNR, field homogeneity, and quality of spectra acquired for single-voxel${ }^{1} \mathrm{H}$spectroscopy). Potential imaging applications for nuclei other than${ }^{1} \mathrm{H}$(e.g.${ }^{13} \mathrm{C},{ }^{19} ~\mathrm{F},{ }^{23} \text{Na}$, and${ }^{31} \mathrm{P}$) were also considered. In addition, we acquired PET-only in ovo images of a chick embryo and compared the images to those obtained with a Siemens Inveon small animal PET/CT. The PET insert was shown to have a homogeneous spatial resolution of$\approx 1.6 ~\text{mm}$paired with a sensitivity of$57.2 \text{kcps} / \text{MBq}$. MR image SNR is$\approx 10 \%$lower when measured with the PET insert installed and running. However, spurious RF noise spectra with running PET are free of detectable interference for${ }^{1} \mathrm{H}$and${ }^{19} ~\mathrm{F}$within the relevant frequency bandwidths. No PET count rate loss was observed for most typical MR sequences. Thus, the BrainPET-7T provides a unique multimodal imaging tool for neuroscience with excellent MR and PET image quality.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".