MR thermometry in the human prostate gland at 3.0T for transurethral ultrasound therapy
Bibliographic record
Abstract
PURPOSE: To investigate the spatial, temporal, and temperature resolution of a segmented gradient echo echo-planar imaging (EPI) technique as applied to proton resonance frequency (PRF) shift thermometry at 3 T in the human prostate gland, and to determine appropriate sequence parameters for magnetic resonance imaging (MRI)-controlled transurethral ultrasound thermal therapy. MATERIALS AND METHODS: Eleven healthy volunteers (age range 23-58) were scanned at 3 T with a 16-channel torso coil to study the behavior of a gradient echo EPI thermometry sequence. The temperature stability and geometric distortion were assessed for 11 different parameter sets. In a further five volunteers, the prostate T2* was measured. RESULTS: For all scan parameters investigated, the temperature standard deviation within the prostate was less than 1°C, while the distortion was less than 1 mm. Temperature stability was best with higher TE values (up to 25 msec), larger voxel sizes and lower EPI factors, but this had to be balanced against requirements for good spatial and temporal resolution. Prostate T2* values ranged from 30-50 msec. CONCLUSION: A good balance between temperature stability and temporal/spatial resolution is obtained with TE = 15 msec, voxel size = 1.14 mm, and EPI factor = 9, resulting in a dynamic scan time of 7.2 seconds for the nine slices.
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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) | 0.002 | 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".