Accelerated PET water activation acquisition with signal separation methodology
Bibliographic record
Abstract
PURPOSE: Positron emission tomography (PET) water activation studies can be considerably accelerated when the time between the successive water bolus injections is reduced. However, when considering shorter interinjection times, as short as 5 min, there will be a contaminating residual PET signal in brain tissues attributed to the previous injections. In this paper a signal separation methodology is proposed to allow shorter (<10 min) interinjection times. METHODS: The contaminating signal in the frames of interest is estimated by extrapolating the decaying tail of the previous signal. The method requires the dynamic PET recording of the decaying signals between two injections in order to extrapolate the tail, which can be done post-reconstruction using a weighted least squares method. Several extrapolation functions are investigated, including a quadratic function, a decaying exponential, and a biologically inspired function. The biologically inspired function is based on the one-tissue compartmental model for [(15)O]H2O and makes use of a generating function estimated from the total trues coincidence rate. To evaluate the proposed method and extrapolation functions, one- and two-dimensional simulation studies were performed, using interinjection times as low as 5 min. The resulting corrected images are compared to the conventionally obtained images with an interinjection time of 10 min, which allows the previous signal to almost completely vanish. RESULTS: Among all considered extrapolation functions the biologically inspired function was found to give the best results. The bias introduced when considering shorter interinjection times (<10 min) could be almost completely removed by subtracting the extrapolated remaining activity from the total measured signal of interest. Compared to the standard method of using longer interinjection times the resulting images have a slightly increased variance. Nonetheless the observed increases are small compared to the total variance and the resulting activation maps were visually very similar. CONCLUSIONS: The simulation results show that the proposed method can significantly reduce the total scan time (e.g., when considering 12 bolus injections, the total scan time can be reduced from 2 to 1 h). Extensive and very realistic simulations were used in this work, paving the way for future in vivo validation of the method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".