Ultrasound settings significantly alter arterial lumen and wall thickness measurements
Bibliographic record
Abstract
BACKGROUND: Flow-mediated dilation (FMD) and carotid intima-medial thickness (CIMT), measured by ultrasound, are widely used to test the efficacy of cardioprotective interventions. Although assessment methods vary, automated edge-detecting image analysis software is routinely used to measure changes in FMD and CIMT. We aimed to quantify the effect that commonly adjusted ultrasound settings have on arterial lumen and wall thickness measurements made with CIMT measurement software. METHODS: We constructed phantom arteries from a tissue-mimicking agar compound and scanned them in a water bath with a 10 MHz multi-frequency linear-array probe attached to a high-resolution ultrasound machine. B-mode images of the phantoms were recorded with dynamic range (DR) and gain set at five decibel (dB) increments from 40 dB to 60 dB and -10 dB to +10 dB respectively. Lumen diameter and wall-thickness were measured off-line using CIMT measurement software. RESULTS: Lumen measurements: there was a strong linear relationship between DR and gain and measured lumen diameter. For a given gain level, a 5 dB increase in DR reduced the measured lumen diameter by 0.02 +/- 0.004 mm (p < 0.001). For a given DR level, a 5 dB increase in gain reduced measured lumen diameter by 0.04 +/- 0.004 mm (p < 0.001). A 5 mm increase in distance between the ultrasound probe and the artery reduced measured lumen diameter by 0.04 +/- 0.03 mm (p < 0.001)CIMT measurements: For a fixed gain level, a 5 dB increase in DR increased measured wall thickness by 0.003 +/- 0.002 mm (p < 0.001). The effects of increasing gain were not consistent and appeared to vary depending on the distance between the artery and the ultrasound probe and the thickness of the artery wall. CONCLUSION: DR, gain and probe distance significantly alter lumen diameter and CIMT measurements made using image analysis software. When CIMT and FMD are used to test the efficacy of cardioprotective interventions, the DR, gain and probe position used to record baseline scans should be documented and replicated in post-treatment scans in individual trial subjects. If more than one sonographer or imaging centre is used to collect data, the study protocol should document specific DR and gain settings to be used in all subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".