Preliminary evaluation of MRI-derived input function for quantitative measurement of glucose metabolism in an integrated PET-MRI
Bibliographic record
Abstract
PET semi-quantitative methods such as relative uptake value can be robust but offer no biological information and do not account for intra-subject variability in tracer administration or clearance. Simultaneous multimodal measurements that combine PET and MRI not only permit crucial multiparametric measurements, it provides means of applying tracer kinetic modelling without the need for serial arterial blood sampling. In this study we adapted an image-derived input function (IDIF) method to improve characterization of glucose metabolism in an ongoing dementia study. Here we present preliminary results in a small group of frontotemporal dementia patients and controls. IDIF was obtained directly from dynamic PET data guided by regions of interest drawn on carotid vessels on high resolution T1-weighted MR Images. IDIF was corrected for contamination of non-arterial voxels. A validation of the method was performed in a porcine model in a PET-CT scanner comparing IDIF to direct arterial blood samples. Metabolic rate of glucose (CMRglc) was measured voxel-by-voxel in gray matter producing maps that were compared between groups. Net influx rate (Ki) and global mean CMRglc are reported. A good correlation (r = 0.9 p<0.0001) was found between corrected IDIF and input function measured from direct arterial blood sampling in the validation study. In 3 FTD and 3 controls, a trend towards hypometabolism was found in frontal, temporal and parietal lobes similar to significant differences previously reported by other groups. The global mean CMRglc and Ki observed in control subjects are in line with previous reports. In general, kinetic modelling of PET-FDG using an MR-IDIF can improve characterization of glucose metabolism in dementia. This method is feasible in multimodal studies that aim to combine PET molecular imaging with MRI as dynamic PET can be acquired along with multiple MRI measurements.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".