Prostate MR elastography with transperineal electromagnetic actuation and a fast fractionally encoded steady‐state gradient echo sequence
Bibliographic record
Abstract
Our aim is to develop a clinically viable, fast-acquisition, prostate MR elastography (MRE) system with transperineal excitation. We developed a new actively shielded electromagnetic transducer, designed to enable quick deployment and positioning within the scanner. The shielding of the transducer was optimized using simulations. We also employed a new rapid pulse sequence that encodes the three-dimensional displacement field in the prostate gland using a fractionally encoded steady-state gradient echo sequence, thereby shortening the acquisition time to a clinically acceptable 8-10 min. The methods were tested in two phantoms and seven human subjects (six volunteers and one patient with prostate cancer). The MRE acquisition time for 24 slices, with an isotropic resolution of 2 mm and eight phase offsets, was 8 min, and the total scan, including positioning and set-up, was performed in 15-20 min. The phantom study demonstrated that the transducer does not interfere with the acquisition process and that it generates displacement amplitudes that exceed 100 µm even at frequencies as high as 300 Hz. In the in vivo human study, average wave amplitudes of 30 µm (46 µm at the apex) were routinely achieved within the prostate gland at 70 Hz. No pain or discomfort was reported. Results in a single patient suggest that MRE can identify cancer tumors, although this result is preliminary. The proposed methods allow the integration of prostate MRE with other multiparametric MRI methods. The results of this study clearly motivate the clinical evaluation of transperineal MRE in patients.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".