Development of a morphologically realistic mouse phantom for pre‐clinical photoacoustic imaging
Bibliographic record
Abstract
BACKGROUND: Characterizations based on anatomically realistic phantoms are highly effective to perform accurate technical validation of imaging systems. Specifically for photoacoustic imaging (PAI), although a variety of phantom models with simplified geometries are reported, an unmet need still exists to establish morphologically realistic heterogeneous pre-clinical phantoms. So the development of a mouse-mimicking phantom can reduce the use of animals for the validation and standardization studies of pre-clinical PAI systems and thus eventually translate the PAI technology to clinical research. PURPOSE: Here we designed, developed, and fabricated a stable phantom that mimics the detailed morphology of a mouse, to be used as a realistic tool for PAI. METHODS: The mouse phantom, has been designed by using a combination of image modeling and 3D-printing techniques. As a tissue-mimicking material, we have used copolymer-in-oil-based material that was recently proposed by the International Photoacoustic Standardization Consortium (IPASC). In particular, the anatomically realistic phantom has been modeled by using the real atlas of a mouse as a reference. The mouse phantom includes a 3D-printed skeleton and the main abdominal organs such as the liver, spleen, and kidneys obtained by using doped copolymer-in-oil material with 3D-printed molds. In addition, the acoustic and optical properties of the tissue-mimicking material and the long-term stability have been broadly characterized. RESULTS: Furthermore, our studies showed that the phantom is durable and stable for more than 200 days, under normal storage and repeated use. Fabrication protocol is easy to reproduce. As a result, the proposed morphologically realistic mouse phantom offers durability, material compatibility, and an unprecedented realistic resemblance to the actual rodents' anatomy in PAI. CONCLUSION: This durable morphologically realistic mouse phantom would minimize the animal experiments in compliance with the 3R principle of Replacement, Reduction, and Refinement. To our knowledge, this is the first time an anatomically realistic heterogeneous mouse phantom has been proposed for PAI in pre-clinical animal imaging and tested its durability over 200 days.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".