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Comparing Phantom and Animal Metrics Applied in the Determination of Focused Ultrasound Stable and Inertial Cavitation Levels

2023· article· en· W4318684554 on OpenAlexafffund
Michael R. Smith, Sonia Khan, Laura Curiel

Bibliographic record

VenueUltrasound in Medicine & Biology · 2023
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaMitacsJadavpur University
KeywordsHydrophoneCavitationImaging phantomSIGNAL (programming language)AcousticsMaterials scienceBiomedical engineeringNoise (video)UltrasoundComputer scienceBiological systemNuclear magnetic resonancePhysicsOpticsArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The opening of the blood-brain barrier (BBB) to allow therapeutic drug passage can be achieved by inducing microbubble cavitation using focused ultrasound (FUS). This approach can be monitored through analysis of the received signal to distinguish between stable cavitation associated with safe BBB opening and inertial cavitation associated with blood vessel damage. In this study, FUS phantom and animal studies were used to evaluate the experimental conditions that generate several cross-consistent metrics having the potential to be combined for the reliable, automatic control of cavitation levels. METHODS: Typical metrics for cavitation monitoring involve observing changes in the spectrum generated by applying the discrete Fourier transform (DFT) to the time domain signal detected using a hydrophone during FUS. A confocal hydrophone was used to capture emissions during a 10 ms FUS burst, sampled at 32 ns intervals, to produce 321,500 points and a high-resolution spectrum when transformed. The FUS spectra were analyzed to show the impact that equipment-transients and well-known DFT-related distortions had on the metrics used for cavitation control. A new approach, physical sparsification (PH-SP), was introduced to sharpen FUS spectral peaks and minimize the effect of these distortions. DISCUSSION: It was demonstrated that the general spectral signal-to-noise ratio (SNR) could be improved by removing the initial noisy phantom hydrophone signal transient. Minor changes in the transient length digitally removed from the sampled values significantly changed the spectral bandwidths of all the harmonically related FUS signals. We evaluated signal processing techniques to minimize the impact these DFT-related distortions on area-under-the-curve (AUC) metric calculations, and we identified the advantages of using PH-SP and proposed new metrics when characterizing FUS spectral properties. The results show many second, third and sub-harmonic metrics provide cross-consistent evidence of changes between stable and inertial cavitation levels. Removing the first harmonic signal component with a hardware low-pass filter allowed the hydrophone gain to be boosted without introducing distortion, leading to an improved analysis of the sub-harmonic signal orders of magnitude smaller in intensity. Metrics that optimized the energy in the real component of the complex-valued PH-SP spectra provided a 32% increase in the sub-harmonic sensitivity compared to standard metrics. CONCLUSION: A preliminary investigation of existing and proposed metrics showed that system noise could be large enough to mask the transition between stable and inertial cavitation. Strong narrowing of sub-harmonic peak shapes on applying physical sparsification (PH-SP) were seen in both phantom and animal studies. However, validating equivalent trends of the metrics with pressure were limited by the increased system noise level in the animal study combined with the natural variability between subjects studied. The combined use of hardware low-pass filters and physical sparsification to selectively removing distortions in the spectrum allowed the optimization of metrics for cavitation monitoring by improving the sub-harmonic sensitivity.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.040
GPT teacher head0.285
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2023
Admission routes2
Has abstractyes

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