Detection of brachytherapy seeds using ultrasound radio frequency signals
Bibliographic record
Abstract
This paper proposed a novel ultrasonic imaging approach for detecting brachytherapy seeds. Accurate and fast seed localization plays a key role in computing dosimetry for prostate brachytherapy. However, currently used B-mode transrectal ultrasound (TRUS) does not adequately visualize implanted seeds, because the diameter of the seed is quite small and visualization is hampered by speckle noise and angulation of the specular reflection of the seeds. Based on the fact that much more ultrasound wave energy is reflected from metal seeds than from other scatterers in tissue, we developed a new seed detection method directly using ultrasound radio frequency (RF) signals (the raw high frequency echoes before the formation of B-mode TRUS images). It monitors the average power (a version of 2-norm) of the RF signals to measure the reflected wave energy. Each RF scan line is subdivided into a sequence of short segments with the same length and spacing. The average power of each segment is computed by the Fourier based spectra or parametric spectral analysis approaches. In the new method, the logarithmic compression is not applied to the raw RF data, and the average power is proportional to the sum of the square of the signal amplitude. Therefore, it produces significantly higher contrast than conventional B-mode TRUS. Furthermore, the average power algorithm can be implemented very efficiently since no numerical optimization is required. Phantom and ex-vivo experiments show that the average power technique successfully detects implanted brachytherapy seeds, and produces superior results compared with B-mode TRUS imaging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".