Microbubble Localization for Three-Dimensional Superresolution Ultrasound Imaging Using Curve Fitting and Deconvolution Methods
Bibliographic record
Abstract
Superresolution algorithms in ultrasound imaging are attracting the interest of researchers recently due to the ability of these methods to enable enhanced vascular imaging. In this study, two superresolution imaging methods are compared for postprocessing images of microbubbles generated using passive acoustic mapping (PAM) methods with a potential application of three-dimensional (3-D) brain vascular imaging. The first method is based on fitting single bubble images one at a time with a 3-D Gaussian profile to localize the microbubbles and a superresolution image is then formed using the uncertainty of the localization as the standard deviation of the Gaussian profile. The second superresolution method is based on image deconvolution that processes multiframe resolution-limited images iteratively and estimates the intensity at each pixel of the superresolution image without the need for localizing each microbubble. The point spread function is approximated by a Gaussian curve which is similar to the beam response of the hemispherical transducer array used in our experimental setup. The Cramér-Rao Bounds of the two estimation techniques are derived analytically and the performance of these techniques is compared through numerical simulations based on experimental PAM images. For linear and sinusoidal traces, the localization errors between the estimated peaks by the fitting-based method and the actual source locations were 220 10 m and 210 5 m, respectively, as compared to 74 10 m and 59 8 m with the deconvolution-based method. However, in terms of the running time and the computational costs, the curve fitting technique outperforms the deconvolution-based approach.
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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".