Reduced power magnetic resonance spectroscopic imaging of the prostate at 4.0 Tesla
Bibliographic record
Abstract
Proton magnetic resonance spectroscopic imaging (MRSI) of the prostate has been described at 1.5 T and 3 T as a means of localizing prostate cancers with high sensitivity and specificity. This technique could be improved by increasing the field strength further; however, it has not been described in detail above 3 T. To address the increase in B1 and SAR at high field strengths, a new protocol is described for reduced power STEAM MRSI of the prostate at 4.0 Tesla, using a pelvic surface coil array for RF transmission and reception, and a solid, reusable endorectal coil for reception only. The optimal STEAM sequence timing parameters for observation of the strongly coupled citrate spin system were determined through simulation to be echo time (TE) = 27 ms and mixing time (TM) = 27 ms, and the results were verified in vitro. Power reduction was achieved by applying the VERSE method to each of the three slice selective pulses in the STEAM sequence, and the B(1)max and SAR were reduced by 43% and 36%, respectively. Finally, in vivo spectroscopic imaging data were acquired from a prostate cancer patient, demonstrating the detection of citrate, choline, and creatine with 0.37 cc nominal resolution in a 10 minute scan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".