Why this work is in the frame
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Bibliographic record
Abstract
The eigen-based filter has theoretically established itself as a potent solution in ultrasound color flow imaging (CFI) for combating against clutter arising from moving tissues. Yet, it remains poorly understood on how much gain in flow detection sensitivity and specificity can be delivered by this adaptive clutter filter. Here, we investigated the receiver operating characteristic (ROC) of the eigen-based clutter filter to statistically evaluate its efficacy. Our investigation was conducted using a new vascular phantom testbed that incorporated both intrinsic tissue motion (vessel pulsation: 7.58 cm/s peak velocity) and extrinsic tissue motion (vibration: 5-Hz frequency, 2.98 cm/s peak velocity), as well as pulsatile flow (pulse rate: 60 beats/min; systolic flow rate: 6.5 mL/s). The eigen-filter (single-ensemble formulation) was applied to CFI raw data sets obtained from the phantom's short-axis view (slow-time ensemble size: 12; pulse repetition frequency: 2 kHz; and ultrasound frequency: 5 MHz), and post-filter Doppler power was compared between flow and tissue regions. Results show that, in the presence of vessel pulsation and tissue vibration, the eigen-filter yielded a high true positive rate in depicting flow pixels in CFI frames (0.945 and 0.917, respectively, during peak systole and end diastole at 60° beam-flow angle), while maintaining a low false alarm rate (0.10) in rendering tissue pixels. Also, the eigen-filter posed ROC curves whose area under curve was higher than those for the polynomial regression filter (statistically significant; t-test p values were less than 0.05). These findings serve well to substantiate the merit of using eigen-filters to enhance the vascular visualization capability of CFI.
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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 it