An optically transparent tissue mimicking phantom for monitoring the thermal lesion produced by high intensity focused ultrasound
Bibliographic record
Abstract
An optically transparent tissue mimicking (TM) phantom whose acoustic properties are close to those of tissue was constructed for visualizing therapeutic effects by high intensity focused ultrasound (HIFU). The TM phantom was designed to improve a prevalent polyacrylamide hydrogel (PAG) which attenuates ultrasound far less than a tissue and does not scatter ultrasound unlike any other tissue. A modified recipe has been proposed in the study by adding scattering glass beads with diameters of 40 ~ 80 μm (0.002 % in w/v) and by raising the concentration of acrylamide (30% in v/v). The constructed TM PAG has an acoustic impedance of 1.66 Mrayls, a speed of sound of 1,573±5 m/s, an attenuation coefficient of 0.53±0.04 dB cm-1MHz-1, a backscattering coefficient of 0.23×10-3 cm-1sr-1 MHz-1 and a nonlinear parameter (B/A) of 5.5±0.2. These parameters are close to those of liver. The thermal and optical properties are almost the same as the prevalent PAG. The TM PAG was tested to visualize the thermal lesions by HIFU and the characteristic features were contrasted with those of the prevalent PAG. In conclusion, the proposed TM PAG acoustically mimics tissue far better than the prevalent PAG and would be expected to be used in assuring if a clinical HIFU device could produce the thermal lesion as planned. Keywords: tissue mimicking phantom, high intensity focused ultrasound (HIFU), ultrasound, polyacrylamide hydrogel (PAG), monitoring, thermal lesion
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".