Super-resolution Imaging with a multifrequency large-aperture hemispherical phased array
Bibliographic record
Abstract
High-resolution imaging of microvasculature is important for diagnostic and therapeutic applications. Here, the utility of a multi-frequency large-aperture hemispherical phased array in the context of vasculature imaging via passive beamforming and super-resolution techniques was explored at various receive frequencies, to investigate the capability of mapping the emissions of lipid coated phase-change nanodroplets flowing through tube phantoms as well as rabbit renal and cerebral vasculatures in-vivo. With super-resolution techniques, the mean lateral (axial) full-width-at-half-maximum image intensity was estimated to be 16 ± 3 (32 ± 6) μm and 7 ± 1 (15 ± 2) μm, corresponding to approximately 1/85 of normal resolution when received with the subarrays at 612 and 1224 kHz, respectively. The mean positional uncertainties were approximately 1/350 (lateral) and 1/180 (axial) of the receive wavelength in water. The renal and cerebral vasculatures of rabbits were partially visualized. A correlation between transmit pulse and nanodroplet vaporization is observed, demonstrating potentials for nanodroplet vaporization modulation and signal-to-noise ratio enhancement. This study demonstrates the feasibility of mapping vaporized nanodroplets with passive beamforming and super-resolution imaging techniques. It may provide means to map complete vasculature pre- or post-treatment with therapeutic focused ultrasound in organs that can be surrounded by the hemispherical array.
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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 | 0.000 |
| 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) | 0.001 | 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".