Localization of spherical lesions in tumor-mimicking phantoms by 3D sparse array photoacoustic imaging
Bibliographic record
Abstract
PURPOSE: The authors have developed a sparse-array photoacoustic imaging (SPAI) system that is capable of mapping 3D distributions of optical absorption using a small number of laser pulses with no mechanical scanning needed. In previous studies, the authors have shown the localization accuracy and the high frame-rate image acquisition on simple phantoms with limited medical relevance. The purpose of this study was to test the imaging capabilities of SPAI in the context of breast tumor detection and localization. METHODS: The authors constructed an array of phantoms that include spherical lesions of sizes 1.5-9 mm, buried in highly scattering tissue phantoms at depths of 3-30 mm. The authors investigated both homogeneous lesions made of blood at varying concentrations and heterogeneous lesions containing vessel-like structures. Volumetric images of the deeply buried lesions were taken at increasingly shallower depths and image-based localization was compared to measured depth. RESULTS: The authors were able to detect and accurately localize homogeneous lesions having a realistic absorption coefficient of 0.2 cm(-1) down to depths of 9-20 mm, and heterogeneous lesions containing 0.5 mm diameter vessel-like structures down to depths of 13-20 mm. Image acquisition required 2.5 s for each volumetric lesion image. CONCLUSIONS: These results suggest that 3D SPAI can detect highly vascularized lesions well below 1 cm in diameter and can overcome optical scatter of tissue to depths of 1-2 cm. With further improvement in the sensitivity and noise characteristics of the imaging system, similar imaging depths should be within reach in real breast tissue. The method, due to its optical contrast, 3D imaging, and fast acquisition, may prove useful in the clinic as an adjunct to existing breast screening tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".