Transit time kinetics in ordered and disordered vascular trees
Bibliographic record
Abstract
Imaging modalities exploit tracer-dilution methods to measure bulk haemodynamic parameters such as blood flow and volume at the level of the microcirculation. Here, we ask the question of whether the kinetics of a tracer can reveal morphological information about the vessels through which the tracers flow. The goal is to relate the acquired time-intensity characteristic to details of the vascular structure that lies below the imaging resolution. Two fractal vascular models are developed that represent organized 'kidney-like' and disorganized 'tumour-like' structures. The models are generated using simple rules of branching and fractal geometry in two dimensions. Blood flow and tracer kinetics are simulated using fundamental laws of haemodynamics. The flow conditions are matched in the two models. The fractal box dimensions of the kidney (D(B) = 1.67 +/- 0.01) and the tumour (D(B) = 1.80 +/- 0.01) vasculatures fall in the range given in the literature (D(B) = 1.61 +/- 0.06 and D(B) = 1.84 +/- 0.04, respectively). The tracer kinetic curves of the kidney and the tumour vasculatures have the same initial slope and final asymptote, corresponding to the same flow rate and vascular volume, but have different forms. The difference in the two curves is related to the distribution function of transit times of the vascular models, and is a consequence of the randomness introduced in vessel diameter and length. In principle, the form of the tracer kinetic curve from a contrast imaging study may offer information relating not only to vascular volume and flow rate, but also to the organization of a microvascular network.
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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".