High‐field MRSI of the prostate using a transmit/receive endorectal coil and gradient modulated adiabatic localization
Bibliographic record
Abstract
PURPOSE: To demonstrate in vivo magnetic resonance spectroscopic imaging (MRSI) of the human prostate at 4.0T using a transmit/receive endorectal coil and a pulse sequence designed specifically for this application. MATERIALS AND METHODS: A solid, reusable endorectal probe was designed for both radiofrequency transmission and reception. Finite difference time domain (FDTD) simulations were performed to characterize the coil's electric field distribution, and temperature measurements were performed in a beef tissue phantom to determine the coil's safe operating limit. The localization by selective adiabatic refocusing (LASER) pulse sequence was implemented using six gradient modulated offset independent adiabatic (GOIA) pulses for very sharp, B(1)-insensitive voxel localization. RESULTS: Based on the simulations and temperature measurements, the coil's safe operating limit was conservatively estimated to be 1.0W for 15 minutes. The transition width of the GOIA pulse selection profiles was only 6% of the bandwidth, compared with 22% for a specific absorption rate (SAR)-matched conventional adiabatic pulse. Using the coil and pulse sequence described here, MRSI data were successfully acquired from a patient with biopsy-proven prostate cancer, with a nominal voxel size of 0.34 cc in a scan time of 15 minutes. CONCLUSION: This work demonstrates the safe and effective use of a transmit/receive endorectal coil for in vivo MRSI of the prostate.
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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 | 0.000 |
| 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) | 0.001 | 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".