Photoacoustic imaging of brachytherapy seeds using a channel-domain ultrasound array system
Bibliographic record
Abstract
Brachytherapy is a technique commonly used in the treatment of prostate cancer that relies on the precise placement of small radioactive seeds near the tumor location. The advantage of this technique over traditional radiation therapies is that treatment can be continuous and uniform, resulting in fewer clinic visits and a shorter treatment duration. Two important phases of this treatment are needle guidance for implantation, and post-placement verification for dosimetry. Ultrasound is a common imaging modality used for these purposes, but it can be difficult to distinguish the seeds from surrounding tissues, often requiring other imaging techniques such as MRI or CT. Photoacoustic imaging may offer a viable alternative. Using a photoacoustic system based on an L7- 4 array transducer and a realtime ultrasound array system capable of parallel channel data acquisition streamed to a multi-core computer via PCI-express, we have demonstrated imaging of these seeds at an ultrasound depth of 16 mm and laser penetration depths ranging up to 50 mm in chicken tissue with multiple optical wavelengths. Ultrasound and photoacoustic images are coregistered via an interlaced pulse sequence. Two laser pulses are used to form a photoacoustic image, and at these depths, the brachytherapy seeds are detected with a signal-to-noise ratio of over 26dB. To obtain this result, 1064nm light was used with a fluence of 100mJ/cm2, the ANSI limit for human skin exposure at this wavelength. This study demonstrates the potential for photoacoustic imaging as a candidate technology for brachytherapy seed placement guidance and verification.
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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) | 0.000 | 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 | 0.000 |
| 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".