S-sequence patterned illumination for fixed-point iterative multiple illumination photoacoustic tomography
Bibliographic record
Abstract
Fixed-point iteration shows promise for quantitative reconstruction of optical absorption in photoacoustic tomography. However, there are issues that prevent the technique from being practical including: non-uniqueness of scattering and absorption profiles, divergence with over-iteration, and sensitivity to noise. Multiple illumination has been proposed to deal with the first problem, and may help with the second. The issue of noise may be balanced out by increasing the regularization parameter at the expense of the exactness of the reconstruction. In a multiple-illumination setup with a circular geometry where fluence is abundant, using a patterned illumination with a decoding step may provide an alternative which will boost SNR. We present a simple sequence of patterned illuminations based on an S-sequence that serves to improve SNR. While the forward model of the iterative method may be applied directly to the patterned excitations, including the decoding step improves SNR in an individual image by a factor equal to the size of the S-sequence, thus greatly improving convergence for a given value of regularization and SNR. For example, with 15 illuminations, 50-60dB noise levels with S-sequence patterned illuminations gives similar simulated performance to the 70dB case with single-source illuminations. This technique will allow the application of fixed-point iteration techniques in a broader range of SNR conditions without resorting to averaging.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".