Adaptation of a Clinical High-Frequency Transrectal Ultrasound System for Prostate Photoacoustic Imaging: Implementation and Pre-clinical Demonstration
Bibliographic record
Abstract
OBJECTIVE: High-frequency, high-resolution transrectal micro-ultrasound (micro-US: ≥15 MHz) imaging of the prostate is emerging as a beneficial tool for scoring disease risk and accurately targeting biopsies. Adding photoacoustic (PA) imaging to visualize abnormal vascularization and accumulation of contrast agents in tumors has potential for guiding focal therapies. In this work, we describe a new imaging platform that combines a transrectal micro-US system with transurethral light delivery for PA imaging. METHODS: A clinical transrectal micro-US system was adapted to acquire PA images synchronous to a tunable laser pulse. A transurethral side-firing optical fiber was developed for light delivery. A polyvinyl chloride (PVC)-plastisol phantom was developed and characterized to image PA contrast agents in wall-less channels. After resolution measurement in water, PA imaging was demonstrated in phantom channels with dyes and biodegradable nanoparticle contrast agents called porphysomes. In vivo imaging of a tumor model was performed, with porphysomes administered intravenously. RESULTS: Photoacoustic imaging data were acquired at 5 Hz, and image reconstruction was performed offline. PA image resolution at a 14-mm depth was 74 and 261 μm in the axial and lateral directions, respectively. The speed of sound in PVC-plastisol was 1383 m/s, and the attenuation was 4 dB/mm at 20 MHz. PA signal from porphysomes was spectrally unmixed from blood signals in the tumor, and a signal increase was observed 3 h after porphysome injection. CONCLUSION: A combined transrectal micro-US and PA imaging system was developed and characterized, and in vivo imaging demonstrated. High-resolution PA imaging may provide valuable additional information for diagnostic and therapeutic applications in the prostate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".