Method for MRI-guided conformal thermal therapy of prostate with planar transurethral ultrasound heating applicators
Bibliographic record
Abstract
A method for conformal prostate thermal therapy using transurethral ultrasound heating applicators incorporating planar transducers is described. The capability to shape heating patterns to the geometry of the prostate gland from a single element in a multi-element heating applicator was evaluated using Bioheat transfer modelling. Eleven prostate geometries were obtained from patients who underwent MR imaging of the prostate gland prior to radical prostatectomy. Results indicate that ultrasound heating applicators incorporating multi-frequency planar transducers (4 x 20 mm, f = 4.7 MHz, 9.7 MHz) are capable of shaping thermal damage patterns to the geometry of individual prostates. A temperature feedback control algorithm has been developed to control the frequency, rotation rate and applied power level from transurethral heating applicators based on measurements of the boundary temperature during heating. The discrepancy between the thermal damage boundary and the target boundary was less than 5 mm, and the transition distance between coagulation and normal tissue was less than 1 cm. Treatment times for large prostate volumes were less than 50 min, and perfusion did not have significant impact on the control algorithm. Rectal cooling will play an important role in reducing undesired heating near the rectal wall. Experimental validation of the simulations in a tissue-mimicking gel phantom demonstrated good agreement between the predicted and generated patterns of thermal damage.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".