Transperineal prostate MR elastography: Initial in vivo results
Bibliographic record
Abstract
This article presents a new approach to magnetic resonance elastography of the prostate using transperineal mechanical excitation. This approach is validated using a prostate elasticity phantom and in vivo studies of healthy volunteers. It is demonstrated that the transperineal approach can generate shear wave amplitudes on the order of 6-30 μm in the mid-gland region. The driver was implemented using an electromagnetic actuator with a hydraulic transmission system. The magnetic resonance elastography acquisition time has been reduced significantly by using a "second harmonic" approach. Displacement fields are processed using the established three-dimensional local frequency estimation algorithm. The three-dimensional curl-based direct inversion was used to calculate the local wavelength. The traveling wave expansion algorithm was used to reconstruct the wave damping image for one case. Using the proposed method, it was possible to resolve lesions of 0.5 cc in the phantom study. Repeatability experiments were performed and analyzed. The results from this study indicate that transperineal magnetic resonance elastography--without an endorectal coil--is a suitable candidate for a patient study involving multiparametric magnetic resonance imaging of prostate cancer, where magnetic resonance elastography may provide additional information for improved diagnosis and image-based surveillance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".