Super-resolution photoacoustic imaging of sparse absorbers using L1-norm minimization (Conference Presentation)
Bibliographic record
Abstract
Delay and sum beamformed acoustic-resolution photoacoustic images are limited in resolution by the wavelength of the received acoustic signal. We seek to improve on this resolution in the case when the received signal is known to be generated by only a few absorbers. When the absorbers to be imaged are known a priori to be sparse then the reconstruction problem can be stated as an optimization problem aiming to minimize the residual between model predictions and measured channel data and also an L1-norm-based metric of sparsity. In brief, the strategy aims to express experimentally observed curved wavefronts in channel data as a super-position of simulated point-spread functions with a constraint on sparsity. The approach is similar in spirit to recent super-resolution contrast ultrasound approaches but uses an L1-norm minimization strategy. We have applied this optimization strategy to photoacoustic beamforming in both simulation and experiment. Simulation was conducted using Field II, and an experimental measure of resolving power was obtained by imaging the cross section of two wires at successively smaller separations. Experimental channel data was acquired using a 21-MHz Visualsonics array transducers with a Verasonics Vantage ultrasound platform for data acquisition. Simulations indicate potential to beat the ultrasound diffraction limit by a factor of four or more while current experiments achieve a factor of two resolution improvement. A possible application of this approach is for providing increased resolution images of the microvasculature surrounding cancerous tumors. Ongoing work aims to investigate in vivo performance of the proposed sparsity-constrained super-resolution approach.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".