Optimization of the Balanced Steady State Free Precession (bSSFP) Pulse Sequence for Magnetic Resonance Imaging of the Mouse Prostate at 3T
Bibliographic record
Abstract
INTRODUCTION: MRI can be used to non-invasively monitor tumour growth and response to treatment in mouse models of prostate cancer, particularly for longitudinal studies of orthotopically-implanted models. We have optimized the balanced steady-state free precession (bSSFP) pulse sequence for mouse prostate imaging. METHODS: Phase cycling, excitations, flip angle and receiver bandwidth parameters were optimized for signal to noise ratio and contrast to noise ratio of the prostate. The optimized bSSFP sequence was compared to T1- and T2-weighted spin echo sequences. RESULTS: SNR and CNR increased with flip angle. As bandwidth increased, SNR, CNR and artifacts such as chemical shift decreased. The final optimized sequence was 4 PC, 2 NEX, FA 50°, BW ±62.5 kHz and took 14-26 minutes with 200 µm isotropic resolution. The SNR efficiency of the bSSFP images was higher than for T1WSE and T2WSE. CNR was highest for T1WSE, followed closely by bSSFP, with the T2WSE having the lowest CNR. With the bSSFP images the whole body and organs of interest including renal, iliac, inguinal and popliteal lymph nodes were visible. CONCLUSION: We were able to obtain fast, high-resolution, high CNR images of the healthy mouse prostate with an optimized bSSFP sequence.
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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) | 0.000 | 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) | 0.002 | 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".