Passive Acoustic Mapping of Microbubbles in Focused Ultrasound Induced Brain Therapies for Preclinical Studies
Bibliographic record
Abstract
Safe and targeted agent delivery to the brain is critical in developing new therapies for highly prevalent brain disorders. Microbubble (MB) - mediated, focused ultrasound (FUS) can temporarily modulate blood-brain barrier permeability (BBBM) to enhance local agent delivery. To explore new brain therapies for clinical uses, preclinical studies in rodent models with precise anatomical targeting and monitoring must be conducted. This thesis discusses the development of a high-resolution transmit-receive FUS array with a novel broadband receiver designed to conduct BBBM treatment for preclinical studies through precise anatomical targeting and acoustic monitoring in rodent models, and feasibility of super localized brain therapy via MB localization with short bursts. A broadband square aperture (1.2 mm^2, thickness = 110 µm) polyvinylidene fluoride receiver was designed and characterized. With the sensitivity per unit area comparable to that of a commercial needle hydrophone in the low megahertz range, a 54° −6 dB acceptance angle at 1.1 MHz, and a low-cost, batch-wise fabrication protocol, the receiver was then used to construct a receive array. An initial ultrasound-propagation simulation study was conducted to optimize the sparse array layout for both transmit and receive. A 256-element sparse hemispherical array (diameter = 100 mm) was constructed by assembling 128 PZT cylinder transmitters (resonance frequency = 1.2 MHz) and 128 broadband PVDF receivers onto a 3D-printed scaffold. The array is able to spatially map MB cloud activity in a vessel-mimicking phantom at sub-, ultra-, and second-harmonic frequencies with high transmit precision. Preliminary in vivo work demonstrated feasibility of this array in inducing localized BBB permeability changes with 3D sub-harmonic MB passive acoustic mapping feedback control in a mouse model. Moreover, the feasibility of super-localized brain therapy using short bursts and localized MBs via a 3D passive acoustic mapping approach. MB cavitation dynamics and feasibility of super resolution imaging and therapy with short bursts were examined with short time passive mapping in vivo. The small form factor phased array and super localized brain therapy technique developed over the course of this thesis are expected to facilitate preclinical studies of FUS-mediated brain therapies to explore novel therapeutic strategies for future clinical applications.
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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 | 0.001 | 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 | 0.001 |
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".