Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging In Vivo
Bibliographic record
Abstract
Super-resolution ultrasound (SRUS) can image microvascular structure and flow at subwave-diffraction resolution based on localizing and tracking microbubbles (MBs). Currently, tracking MBs accurately under limited imaging frame rates and high MB concentrations remains a challenge, especially under the effect of cardiac pulsatility and in highly curved vessels. In this study, an acceleration-incorporated MB motion model is introduced into a Kalman tracking framework. The tracking performance was evaluated using simulated microvasculature with different MB motion parameters, concentrations, and acquisition frame rates, and in vivo human breast tumor US datasets. The simulation results show that the acceleration-based method outperformed the nonacceleration-based method at different levels of acceleration and acquisition frame rates and achieved significant improvement in true positive rate (TPR; up to 11.3%) and false negative rate (FNR; up to 13.2%). The proposed method can also reduce errors in vasculature reconstruction via the acceleration-based nonlinear interpolation, compared with linear interpolation (up to [Formula: see text]). The tracking results from temporally downsampled low frame rate in vivo datasets from human breast tumors show that the proposed method has better MB tracking performance than the baseline method, if using results from the initial high frame data as a reference. Finally, the acceleration estimated from tracking results also provides a spatial speed gradient map that may contain extra valuable diagnostic information.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".