Impact of erroneous kinetic model formulation in Direct 4D image reconstruction
Bibliographic record
Abstract
Direct parametric image reconstruction has the potential to reduce variance in parameter estimates when applied to PET/CT data. One complication when estimating parametric maps in the body is the difficulty of finding one single model to describe all the different kinetics in the field of view (FOV). Contrary to the post-reconstruction kinetic analysis though, any errors (bias) from the discrepancy between the model and the observed kinetics in the direct 4D reconstruction can potentially propagate spatially from unimportant areas to areas of interest. In this work we investigate this effect on simulated 4-D datasets based on a digital body phantom. Different realistic cases were simulated including differential input functions in the FOV and organs with different kinetics. Micro-parameters (K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ,Vd, bv) where estimated using a newly proposed spatiotemporal 4D image reconstruction algorithm as well as using post-reconstruction kinetic analysis on noiseless and noisy datasets simulating [ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">15</sup> O] H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O kinetics in the body. Bias analysis both in noiseless and noisy data showed a bias from badly modelled areas spatially propagates to other regions of interest in the direct reconstruction. Critically though under noisy conditions even with the bias propagation, the direct reconstruction method still outperforms the conventional post-reconstruction methodology. Nevertheless there is a need to ensure that appropriate models are chosen to describe the kinetics in the entire FOV with approaches such as data-driven adaptive kinetic modelling worth exploring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".