Investigation of hardware and software techniques to enhance the characteristics of focused ultrasound (FUS) spectra
Bibliographic record
Abstract
Abstract Objective . Microbubble cavitation generated by focused ultrasound ( FUS ) can induce safe blood-brain-barrier ( BBB ) opening allowing therapeutic drug passage. Spectral changes in the hydrophone sensor signal are currently used to distinguish stable cavitation from inertial cavitation that can damage the BBB. Gibbs’ ringing, peak intensity loss and peak width increase are well-known distortions evident when using the discrete Fourier transform ( DFT ) to transform data containing a few hundred points. We investigate overcoming the fact that FUS time signals (10 ms providing 312 500 points sampled at 32 ns intervals) can generate such sharp spectral peaks that variations in their DFT- related distortions can significantly impact the values of the key metrics used for cavitation characterization. Approach . We introduce low-pass filter hardware to improve how the analogue to digital convertor handles high-frequency noise components and the orders of magnitude differences between FUS harmonic intensities. We investigate the enhanced FUS spectral stability and resolution obtained from a new technique, physical sparsification (PH-SP), customized to the a-priori information that all key FUS components are harmonically related. Results are compared with standard DFT optimizations involving time data windowing and Fourier interpolation. Main results . A new simulation model showed peak intensity, widths and metrics modified by small changes in the transformed signal’s length when removing the noisy starting transient of the FUS hydrophone signal or following minor excitation frequency or sampling rate adjustments. 25%–60% area-under-the-curve changes occurred in phantom studies at different pressure levels. Spectral peak sharpness was best optimized and stabilized with PH-SP . Significance . Special FUS characteristics mean starting transients and minor variations in experimental procedures lead to significant changes in the spectral metrics used to monitor cavitation levels. Customizing PH-SP to these characteristics led to sharper, more stable spectra with the potential to track the impact of microbubble environment changes.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".