SU‐E‐I‐26: Development and Characterization of a Temporally Stable Tissue‐ Mimicking Photoacoustic‐Ultrasonic Phantom
Bibliographic record
Abstract
Purpose: To quantify the tissue‐mimicking properties and temporal stability of a novel tissue‐mimicking photoacoustic‐ultrasonic (PAUS) imaging phantom. Methods: Nine phantoms containing titanium wire targets were formulated from a Zerdine base with varying degrees of Intralipid and India ink dye to generate distinct scattering and absorption characteristics. India ink was included in three concentrations (0.01%, 0.005%, or 0.0025% by mass), while Intralipid comprised 12.5%, 25%, or 37.5% of the sample by mass. These scattering/absorption agents were combined in a 3×3 matrix to generate unique properties for each phantom. Speed‐of‐sound (SOS), acoustic absorption and optical scattering/absorption measurements were obtained for each formulation to characterize tissuemimicking properties. PAUS imaging was performed on each phantom at four time points, in approximately 2‐month intervals, to assess temporal stability. Images were analyzed to provide data regarding target signal strength, target contrast, signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR). All PAUS imaging was performed on a Vevo 2100‐LAZR system (FUJIFILM VisualSonics Inc., Toronto, Canada) at 808 nm using a 20‐MHz US transducer. Results: Each phantom formulation provided unique acoustic absorption and optical scattering/absorption; the range of tested formulations provided scattering/absorption/SOS characteristics that accurately mimic tissue. Examination of the longitudinal PAUS data demonstrates that none of the phantoms experienced significant temporal change over the 6‐month testing period. Target signal, contrast, SNR, and CNR changed less than 10% from the initial baseline for an individual phantom through 2, 4, and 6‐month follow‐up imaging sessions. Conclusion: This work indicates that the investigated phantom formulations accurately approximate tissue for photoacoustic‐ultrasonic imaging purposes. Additionally, adequate temporal stability is demonstrated over a six‐month period, allowing for construction of a long‐term tissuemimicking PAUS imaging phantom that could be utilized for preclinical technical development or clinical quality assurance purposes in the future. John Lynch is an employee of CIRS, Inc.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".