Analysis of the spatial and temporal accuracy of heating in the prostate gland using transurethral ultrasound therapy and active MR temperature feedback
Bibliographic record
Abstract
A new MRI-guided therapy is being developed as a minimally invasive treatment for localized prostate cancer utilizing high-intensity ultrasound energy to generate a precise region of thermal coagulation within the prostate gland. The purpose of this study was to evaluate in vivo the capability to produce a spatial heating pattern in the prostate that accurately matched the shape of a target region using transurethral ultrasound heating and active MR temperature feedback. Experiments were performed in a canine model (n = 9) in a 1.5 T MR imager using a prototype device comprising a single planar transducer operated under rotational control. The spatial temperature distribution, measured every 5 s with MR thermometry, was used to adjust the acoustic power and rotation rate in order to achieve a temperature of 55 degrees C along the outer boundary of the target region. The results demonstrated the capability to produce accurate spatial heating patterns within the prostate gland. An average temperature of 56.2 +/- 0.6 degrees C was measured along the outer boundary of the target region across all experiments in this study. The average spatial error between the target boundary and the 55 degrees C isotherm was 0.8 +/- 0.7 mm (-0.2 to 3.2 mm), and the overall treatment time was < or =20 min for all experiments. Excellent spatial agreement was observed between the temperature information acquired with MRI and the pattern of thermal damage measured on H&E-stained tissue sections. This study demonstrates the benefit of adaptive energy delivery using active MR temperature feedback, and an excellent capability to treat precise regions within the prostate gland with this technology.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".