Robust cerebrovascular blood velocity and flow rate estimation from 4D‐<scp>CTA</scp>
Bibliographic record
Abstract
PURPOSE: Blood velocity and flow rate information may be helpful for a wide variety of applications, but it often requires separate acquisitions. The dynamic information combined with the high spatial resolution of four-dimensional computed tomography angiography (4D-CTA) offers the possibility to quantify blood flow simultaneous to vascular anatomy. METHODS: A 4D-CTA clinical protocol with a novel dedicated postprocessing were validated in vitro in a patient-specific model, and tested in a pilot study of six patients. Blood flow was assessed in both internal carotid (ICAs) and vertebral (VAs) arteries by analyzing spatial displacement of contrast agent in the form of time-intensity curves (TICs). Unlike previous approaches, it does not require any a priori assumptions about TIC shape, but rather computes mean velocity and flow rates from the spatial displacement of the TICs along the automatically segmented vessels. RESULTS: In vitro experiments showed good agreement between 4D-CTA and flowmeter measurements under steady and pulsatile flow conditions. In vivo measurements exhibited large interpatient variability of the TIC shapes, from which blood flow rates could nevertheless be successfully measured in all patients and investigated vessels. On average, measured flow rates were 3.2 ± 0.7 ml/s (in ICAs) and 1.3 ± 0.8 ml/s (in VAs) consistent with previous reference standards. Contrary to our novel approach, which considered the full TIC shape, application of previous time-to-peak analyses based on idealized TIC shapes showed limited reliability. CONCLUSIONS: We demonstrate the high potential of 4D-CTA for assessing blood velocity and flow rate in addition to anatomical evaluation. The wide variety of TIC shapes encountered in vivo highlights the importance of an adaptive TIC analysis as proposed in the present work.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".